hany mohamed aziz elzahar determinants and consequences of ... and corrected... · hany mohamed...

Hany Mohamed Aziz Elzahar

Determinants and Consequences of Key Performance

Indicator (KPI) Reporting by UK Non-financial Firms

Thesis Submitted in Fulfilment for the Degree of Doctor of

Philosophy

19th

November 2013

Accounting and Finance Division

Stirling Management School

University of Stirling

United Kingdom

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Abstract

The study examines the level of the quantity and quality of Key Performance Indicator

(KPI) reporting for a sample of FTSE 350 UK listed companies over the period 2006-

2010. Furthermore, it identifies the determinants of KPI reporting and investigates its

impact upon firm value. Based upon best practice guidance recommended by the

Accounting Standard Board (2006), the study develops a measure of disclosure quality

by considering the main qualitative attributes of information which, arguably, make

KPI information particularly useful to stakeholders. The distinction between disclosure

quantity and quality in the study enables the researcher to obtain greater insights into

the drivers and implications of KPI reporting quantity and quality. The study finds a

variation between UK firms in the number of KPIs disclosed, with a notable low level

of reporting quality, especially in the case of non-financial KPIs. It also finds that

corporate governance mechanisms play an important role in improving KPI reporting.

In particular, it shows that directors’ compensation affects the quantity and quality of

KPI disclosure. Furthermore, the study provides evidence that the quantity and quality

of KPI disclosure are not derived from the same factors, and both have a different

impact on firm value. On the other hand, the study finds a negative association between

the numbers of KPIs disclosed and firm value, while a non-significant relationship is

reported between KPI reporting quality and firm valuation. Overall, this study provides

evidence that disclosure quantity is not a good proxy for disclosure quality.

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Attestation

I confirm that I have today submitted 2 hard copies of my thesis. I hereby declare that

no portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university.

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حيم حمن الره الره بسم للاه

ا خير أمه وسلم على عباده الهذين اصطفى آلله ماوات والرض وأنزل لكم 95يشركون )قل الحمد لله ن خلق السه ( أمه

إله مع للاه ماء ماء فأنبتنا به حدائق ذات بهجة ما كان لكم أن تنبتوا شجرها أ ن 06 بل هم قوم يدلون )من السه ( أمه

إله م جل بل أكثرهم ل الرض قرارا وجل خللها أنهارا وجل لها رواسي وجل بين البحرين حاجزا أ ع للاه

ن يجيب المضطره إذا دعاه ويكشف السوء ويجلكم خل 06يلمون ) رون ( أمه قليل ما تذكه إله مع للاه فاء الرض أ

إله 06) ياح بشرا بين يدي رحمته أ ن يهديكم في ظلمات البر والبحر ومن يرسل الر ا ( أمه عمه تالى للاه مع للاه

ن 06يشركون ) قل هاتوا برها( أمه إله مع للاه ماء والرض أ يده ومن يرزقكم من السه نكم إن كنتم يبدأ الخلق ثمه ي

وما يشرون 06صادقين ) ماوات والرض الغيب إله للاه (09أيهان يبثون ) ( قل ل يلم من في السه

In the name of Allah, Most Gracious, Most Merciful.

‘ (95) Say: Praise be to Allah and Peace on his servants whom He has chosen (for his

Message). Who is better? - Allah or the false gods they associate with Him? (06) Or,

who has created the heavens and the earth, and who sends you down rain from the sky

causing the growth of well-planted orchards, full of beauty of delight: it is not in your

power to cause the growth of the trees in them. Can there be another god besides

Allah? Indeed, they are a people who swerve from justice. (06) Or, who has made the

earth firm to live in; made rivers in its midst; set thereon mountains immovable; and

made a separating bar between the two bodies of flowing water? Can there be another

god besides Allah? No, but most of them do not know. (06) Or, who listens to the (soul)

distressed when it calls on Him, and Who relieves its suffering, and makes you

(mankind) inheritors of the earth? Can there be another god besides Allah? Little you

remember. (06) Or, who guides you through the depths of darkness on land and sea,

and who sends the winds as heralds of glad tidings, going before the rain? Can there be

another god besides Allah? - High is Allah above what they associate with Him! (06)

Or, who originates creation, then repeats it, and who gives you sustenance from heaven

and earth? Can there be another god besides Allah? Say, "Bring forth your argument, if

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you are telling the truth!" (09) Say: None in the heavens or on earth, except Allah,

knows the unseen nor can they perceive when they shall be raised up for Judgment’(The

Holy Qur’an, Chapter 27 , Verses 59-65).

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Acknowledgements

In the name of Allah, the Lord of Mercy, the Giver of Me, and Peace and Blessings of

Allah Be Upon the Prophet. Above all, I would like to thank Allah, who has empowered

me and enabled me to complete this thesis successfully.

This thesis is the result of my doctoral research at the Accounting and Finance Division

of the Stirling Management School at the University of Stirling from 2010 to 2013. I

have benefited from many people’s support, encouragement, and guidance. I would like

to seize this opportunity to thank all of those who have driven me to produce this work.

I would like to express my greatest and most profound appreciation to my supervisors,

Professor Khaled Hussainey and Dr Ioannis Tsalavoutas, for their excellent supervision

during the course of this research. This thesis could not have been completed without

their guidance, patience, time and enthusiasm throughout the various stages of my PhD.

I am very fortunate to have been supervised by Professor Hussainey. I have obtained a

great deal in terms of invaluable knowledge and skills as a result of his academic

expertise. I really enjoyed brainstorming new research ideas with Professor Hussainey.

I will never forget his unwavering and invaluable support, encouragement and

suggestions, not only with regard to issues relevant to this study, but also to issues

related to how to approach life.

Very special thanks go to my second supervisor, Dr Ioannis Tsalavoutas. Dr

Tsalavoutas consistently provided me with valuable comments and directions in terms

of my work. His encouragement, guidance and support undoubtedly resulted in

significant contributions to the development of this thesis.

I would like to express my sincerest gratitude to my internal examiner, Professor Ian

Fraser, and my external examiner, Dr Basil Al-Najjar (Birkbeck, University of

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London), for their time and invaluable comments on my thesis, as well as for their

positive input.

My thanks should also go to Damietta University and the Ministry of Higher Education

in Egypt for the financial support given to me throughout the stages of my PhD studies.

I am really grateful to the Cultural and Educational Counselor, Cultural Attaché, and all

the administrative staff at the Egyptian Educational and Cultural Bureau in London for

their great financial support and educational supervision during the period of my

scholarship (2009-2013).

I also take this opportunity to give my special appreciation to all administrative and

academic staff at the Stirling Management School, and in the Accounting and Finance

Division in particular, for their support. They have provided me with a healthy

environment that helped me to achieve my ambition.

I would like to express my sincerest gratitude to all participants of the workshops and

conferences at which I have presented my work during these last few years. I gratefully

acknowledge the constructive advice of Professor Ian Fraser at the early stages of this

study to extend my sample firms from FTSE 100 to FTSE 350 UK firms. I owe

heartfelt thanks to Louise Crawford, Lisa Evans, Pauline Weetman and Richard Slack,

for their helpful comments and recommendations. I acknowledge the helpful comments

received from the participants at the Accounting and Finance Division research

seminars (Stirling University, June 2011 and November 2012), the 2011 ICAS

Research Development Event (Edinburgh), the BAA Scot-Doc conferences (Edinburgh,

2011), the 2011 and 2012 SGRS conferences (Stirling University), The BAFA Scot-

Doc conference (Strathclyde Business School, June 2012), the Financial Reporting and

Business Communication Research 16th Annual Conference (Bristol University, July

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2012), and the 36th Annual Congress of the European Accounting Association (Paris,

May 2013).

Very special thanks should also go to Dr. Stuart McChlery (Glasgow Caledonian

University), Professor Catriona Paisey (University of Glasgow) and Professor Khaled

Hussainey (my principal supervisor) who gave me the opportunity to work with them as

a research assistant in their project ‘Disclosure on Oil and Gas reserves in UK

companies’ in 2010-2011. It added to my experience and also helped me to improve my

research skills.

My deepest gratitude and thanks should also go to my parents, Mr Mohamed Aziz

Elzahar and Mrs Mervat Elesawy, for their infinite love, sacrifice and support. Most of

what I have achieved in my life so far is because of their prayers and endless

encouragement. I am also very grateful to my brothers and sisters for their continuous

love and moral support. I would like to express my gratitude to my lovely children,

Tarek, Mennah, Amr, and Hala for their love and patience during my PhD studies. They

kept me cheerful at all times with their playfulness, smiles and laughter. I love them

dearly, and I hope they will be successful in their lives.

I am deeply grateful to my friend Dr Alaa Zalata for his friendship, help, support and

encouragement during the course of this research. I very much appreciate how he

inspired me with his words which are filled with dedication and discipline towards my

work. Special thanks should go to Dr Alaa Zalata, and my colleague, Mr. Mohamed

Hasan, at the Centre for Translation Studies, University of Leeds, for proof-reading this

thesis. Also, my thanks go to my colleagues in the Accounting and Finance Division at

the University of Stirling. They have played a major role in my development on a

scientific and a personal level during the course of writing this thesis.

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Last but not least, I am delighted to thank all my close friends in the UK and in my

beloved country, Egypt, for their continuous encouragement and contribution to my

ideas.

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List of Contents

Abstract.............................................................................................................................i

Attestation........................................................................................................................ii

Acknowledgements .........................................................................................................v

List of Tables ................................................................................................................xiii

Chapter 1- Introduction .................................................................................................1

1.1 Overview ........................................................................................................... 1

1.2 The nature of the study ...................................................................................... 4

1.3 Motivations ........................................................................................................ 5

1.4 Research questions ............................................................................................ 8

1.5 Research objectives and contributions ............................................................ 10

1.5.1 Research objectives ............................................................................................. 10

1.5.2 Research contributions ........................................................................................ 11

1.6 Organisation of the study................................................................................. 14

Chapter 2 - KPI reporting in the UK: Descriptive statistics.....................................17

2.1 Overview ......................................................................................................... 17

2.2 Regulatory framework & literature review ..................................................... 18

2.2.1 Regulatory framework & previous studies.......................................................... 18

2.2.2 Measuring KPI reporting quality......................................................................... 27

2.3 Methods ........................................................................................................... 35

2.3.1 Designing the research instrument & ensuring its validity ................................. 45

2.3.2 Assessing the reliability of the research instrument: pilot study......................... 45

2.4 Data.................................................................................................................. 47

2.5 Findings of the analysis ................................................................................... 52

2.5.1 Companies’ disclosures....................................................................................... 53

2.5.2 Descriptive statistics for quantity of KPI reporting ............................................ 55

2.5.3 Descriptive statistics for quality of KPI reporting .............................................. 64

2.5.4 Correlation between KPI reporting quantity and its quality ............................... 73

2.6 Discussion and overall conclusions ................................................................. 74

Chapter 3 - The determinants of KPI reporting in the UK ......................................80

3.1 Overview ......................................................................................................... 80

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3.2 Theories that explain KPI reporting ................................................................ 81

3.2.1 Agency theory ..................................................................................................... 81

3.2.2 Signalling theory ................................................................................................. 83

3.2.3 Capital need theory.............................................................................................. 84

3.2.4 Political cost theory ............................................................................................. 85

3.2.5 Stakeholder theory............................................................................................... 86

3.2.6 Information costs theory...................................................................................... 88

3.2.7 Summary of relevant theories ............................................................................. 88

3.3 Previous literature............................................................................................ 90

3.4 Research hypotheses........................................................................................ 93

3.4.1 Directors’ compensation ..................................................................................... 95

3.4.2 Board characteristics ........................................................................................... 96

3.4.3 Audit committee characteristics ........................................................................ 100

3.4.4 Ownership structure .......................................................................................... 101

3.4.5 Capital need variables ....................................................................................... 103

3.4.6 Firm characteristics as control variables ........................................................... 105

3.5 The data, descriptive statistics, and the models............................................. 110

3.5.1 The data ............................................................................................................. 110

3.5.2 Descriptive statistics.......................................................................................... 113

3.5.3 Econometric procedures .................................................................................... 117

3.5.4 Regression models............................................................................................. 119

3.6 The main analyses ......................................................................................... 121

3.6.1 Econometric procedures .................................................................................... 121

3.6.2 Empirical results................................................................................................ 125

3.7 Discussion of the results ................................................................................ 133

3.7.1 Directors’ compensation variables .................................................................... 133

3.7.2 Board characteristics ......................................................................................... 134

3.7.3 Audit committee characteristics ........................................................................ 137

3.7.4 Ownership structure .......................................................................................... 138

3.7.5 Capital need variables ....................................................................................... 139

3.7.6 Firm characteristics (control variables) ............................................................. 141

3.8 Further analysis ............................................................................................. 144

3.8.1 The determinants of total non-financial KPI reporting ..................................... 144

3.8.2 The determinants of total KPI reporting ........................................................... 145

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3.9 Conclusion ..................................................................................................... 146

Chapter 4 - The impact of KPI reporting on firm value .........................................148

4.1 Overview ....................................................................................................... 148

4.2 Theory and previous literature....................................................................... 149

4.2.1 Theory ............................................................................................................... 149

4.2.2 Previous literature ............................................................................................. 153

4.3 Research hypotheses...................................................................................... 160

4.3.1 KPI reporting quantity and quality & firm value .............................................. 160

4.3.2 Corporate governance mechanisms and firm value .......................................... 164

4.3.3 Firm characteristics and other control variables ............................................... 169

4.4 Data, regression models, and descriptive statistics ....................................... 170

4.4.1 The data ............................................................................................................. 170

4.4.2 The models ........................................................................................................ 172

4.4.3 Descriptive statistics.......................................................................................... 175

4.5 The main analyses ......................................................................................... 181

4.5.1 The association between firm value and KPI reporting .................................... 181

4.5.2 The association between firm value and reporting on KPI subcategories ........ 190

4.6 Further analyses ............................................................................................. 197

4.6.1 Firm value & total non-financial KPI reporting................................................ 197

4.6.2 Firm value & total KPI reporting ...................................................................... 199

4.7 Conclusion ..................................................................................................... 201

Chapter 5 - Concluding remarks ...............................................................................204

5.1 Overview ....................................................................................................... 204

5.2 Summary of research questions, objectives and approach ............................ 204

5.2.1 Research questions ............................................................................................ 205

5.2.2 Research objectives ........................................................................................... 205

5.2.3 Research approach............................................................................................. 206

5.3 Research findings .......................................................................................... 208

5.3.1 The attributes of KPI reporting in the UK (Q1) ................................................ 208

5.3.2 Factors explaining the variation in KPI reporting (Q2) .................................... 210

5.3.3 KPI reporting and firm value (Q3) .................................................................... 212

5.3.4 Quantity and quality of financial reporting (Q4) .............................................. 214

5.4 Contributions and implications...................................................................... 215

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5.4.1 Contributions ..................................................................................................... 215

5.4.2 Implications ....................................................................................................... 217

5.5 Limitations of the study ................................................................................. 220

5.6 Suggestions for future research ..................................................................... 223

References ....................................................................................................................226

Appendix 1 ....................................................................................................................... I

Appendix 2 ...............................................................................................................XVIII

Appendix 3 ............................................................................................................... XXIX

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List of Tables

Table 1 Overview of the dimensions used to measure KPI reporting quality in relation

to qualitative characteristics of information.................................................................... 40

Table 2 An example of using the research instrument to assign quantity and quality in

terms of KPI reporting .................................................................................................... 44

Table 3 ANOVA and Kruskal-Wallis tests of differences among quantity and quality

scores in the pilot study................................................................................................... 47

Table 4 Sample selection and its disaggregation across industries................................. 49

Table 5 Study variables: definitions & measurement ..................................................... 50

Table 6 Descriptive statistics of continuous variables .................................................... 52

Table 7 Descriptive statistics for KPI Quantity scores ................................................... 56

Table 8 The frequency of KPI disclosed by sample firms ............................................. 56

Table 9 Quantity of KPI reporting across years.............................................................. 58

Table 10 Anova test to compare KPI reporting quantity across sample period .............. 60

Table 11 Quantity of KPI reporting across industries .................................................... 61

Table 12 Descriptive statistics for KPI quality scores .................................................... 64

Table 13 KPI reporting quality: descriptive statistics of individual dimensions ............ 65

Table 14 Quality of KPI reporting across years.............................................................. 67

Table 15 Anova test to compare KPI reporting quality across sample period ................ 69

Table 16 Quality of KPI reporting across industries ...................................................... 71

Table 17 Pearson correlation matrix ............................................................................... 73

Table 18 Explanatory variables and their expected relationship with KPI disclosure

based on previous studies.............................................................................................. 109

Table 19 Sample selection and its disaggregation across industries............................. 111

Table 20 The definition and measurement of explanatory variables ............................ 112

Table 21 Descriptive statistics of explanatory variables............................................... 114

Table 22 Descriptive statistics of dependent variables ................................................. 116

Table 23 Pearson correlation matrix for explanatory variables .................................... 120

Table 24 KPI reporting in KPI section: the determinants of quantity .......................... 126

Table 25 KPI reporting in KPI section: the determinants of quality ............................ 127

Table 26 The findings in terms of directors’ compensation variables ......................... 133

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Table 27 The findings in terms of board characteristics variables .............................. 134

Table 28 The findings with regard to AC characteristics ............................................ 137

Table 29 The findings in terms of capital need variables ............................................ 140

Table 30 Sample selection and its disaggregation across industries ............................. 171

Table 31 Study variables: definitions & measurement ................................................. 175

Table 32 Descriptive statistics for study variables ....................................................... 177

Table 33 Pearson correlation matrix ............................................................................. 180

Table 34 Firm value (TQ) & KPI reporting quantity in KPI section ............................ 182

Table 35 Firm value (TQ) & KPI reporting quality in KPI section .............................. 186

Table 36 Firm value (TQ) & financial KPI reporting quantity.................................... 191

Table 37 Firm value (TQ) & financial KPI reporting quality....................................... 192

Table 38 Firm value (TQ) & quantity of non-financial KPI reported in KPI section .. 195

Table 39 Firm value (TQ) & quality of non-financial KPI reported in KPI section .... 196

Table 40 Firm value (TQ) & quantity and quality of KPI reporting ............................ 200

Table 41 Anova test to compare financial KPI reporting quantity across sample period ..I

Table 42 Anova test to compare non-financial KPI reporting quantity across sample

period ............................................................................................................................... II

Table 43 Anova test to compare total non-financial KPI reporting quantity across

sample period .................................................................................................................. III

Table 44 Anova test to compare total KPI reporting quantity across sample period ..... IV

Table 45 Anova test to compare financial KPI reporting quality across sample period.. V

Table 46 Anova test to compare non-financial KPI reporting quality across sample

period .............................................................................................................................. VI

Table 47 Anova test to compare total non-financial KPI reporting quality across sample

period .............................................................................................................................VII

Table 48 Anova test to compare total KPI reporting quality across sample period .... VIII

Table 50 Anova test to compare financial KPI reporting quantity across industries...... IX

Table 51 Anova test to compare non-financial KPI reporting quantity across industries X


industries ......................................................................................................................... XI

Table 53 Anova test to compare KPI reporting quantity across industries....................XII

Table 54 Anova test to compare total KPI reporting quantity across industries ......... XIII

Table 55 Anova test to compare financial KPI reporting quality across industries..... XIV

Table 56 Anova test to compare non-financial KPI reporting quality across industries

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....................................................................................................................................... XV

Table 57 Anova test to compare total non-financial KPI reporting quality industries . XV

Table 58 Anova test to compare KPI reporting quality across industries.................... XVI

Table 59 Anova test to compare total KPI reporting quality across industries .......... XVII

Table 60 Determinants of financial KPI reporting quantity ........................................ XIX

Table 61 Determinants of financial KPI reporting quality ........................................... XX

Table 62 Determinants of non-financial KPI reporting quantity ................................. XXI

Table 63 Determinants of non-financial KPI reporting quality .................................. XXII

Table 64 The determinants of total non-financial KPI reporting quantity................ XXIV

Table 65 The determinants of total non-financial KPI reporting quality....................XXV

Table 66 The determinants of total KPI reporting quantity ...................................... XXVI

Table 67 The determinants of total KPI reporting quality ....................................... XXVII

Table 68 Firm value (TQ) & total non-financial KPI reporting quantity ................ XXIX

Table 69 Firm value (TQ) & total non-financial KPI reporting quality .....................XXX

Table 70 Firm value (TQ) & total KPI reporting quantity........................................ XXXI

Table 71 Firm value (TQ) & total KPI reporting quality......................................... XXXII

CHAPTER ONE: INTRODUCTION

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Chapter 1- Introduction

1.1 Overview

In recent years, a sizeable body of literature indicates a major increase in interest in

narrative reporting (e.g. Solomon et al., 2000, Hussainey et al., 2003; Beattie et al.,

2004; Linsley and Shrives, 2006; Abraham and Cox, 2007; Hussainey and Walker,

2009; Hussainey and Al-Najjar 2011; Wang and Hussainey, 2013). These studies show

that narrative reporting plays an important role in improving communications with

shareholders. It explains financial performance and provides a broader analysis of a

firm’s operating activities, any surrounding risks, objectives, prospects and strategies.

Thus, it increases the relevance of corporate reporting by complementing the role of

financial statements.

In the United Kingdom (UK), firms have been requested to introduce an Operating and

Financial Review (OFR) statement that includes analyses of the firm’s business position

and development. However, UK firms provide this statement on a voluntary basis in

accordance with the financial reporting statement issued by the Accounting Standard

Board (ASB) in 2006.

Key Performance Indicators (KPIs) are crucial measures of performance that are

disclosed by a firm’s directors in order to help the stakeholders to analyse the firm’s

performance. These KPIs could be financial (e.g. operating profit, cash flow, and

earnings per share), or non-financial (e.g. new product launches; emissions; total

energy). KPI information is one of the main narrative disclosures that could be useful

to the users of annual reports. By analysing KPI information, different users could

evaluate the financial performance of the firm and assess the current competitive

position, as well as the extent to which progress is being made in accordance with the

firm’s strategy.


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In particular, in the UK, the Companies Act (CA) (2006) in accordance with the

European Union (EU) Accounts Modernization Directive (2003) requires that all

companies, with the exception of small firms, should review their business using

financial KPIs and, where appropriate, using other KPI information with regard to

environmental and employee aspects. Accordingly, many regulatory bodies (e.g. the

ASB) require firms to report this information. Furthermore, the ASB provides guidance

for achieving best practice in terms of KPI disclosure in the OFR (ASB, 2006).

In general, most research papers to date have examined the determinants and economic

consequences of either overall disclosure or particular types of disclosure. Numerous

studies have examined the factors affecting corporate narrative disclosure, suggesting

that corporate governance (CG) attributes, as well as firm characteristics, are the key

drivers for corporate disclosure, either as a whole (e.g. Ho and Wong, 2001; Wang et

al., 2008) or for different types of corporate disclosure such as forward looking

statements (e.g. Wang and Hussainey, 2013); risk reporting (e.g. Abraham and Cox,

2007; Elshandidy et al., 2013); online reporting (e.g. Abdelsalam and Street, 2007); and

intellectual capital disclosure (e.g. Li et al., 2008). The other stream of research has

shown the usefulness of corporate disclosure on stock market participants’ decisions.

For instance, several studies tested the effect of corporate disclosure on cost of capital

(e.g. Welker, 1995; Botosan, 1997; Botosan and Plumlee, 2002); firm value (e.g. Baek

et al., 2004; Hassan et al., 2009); share price anticipation of earnings (e.g. Hussainey

and Walker, 2009; Wang and Hussainey, 2013) and analysts’ following, besides

analysts’ forecast accuracy (e.g. Lang and Lundholm, 1996; Eng and Teo, 2000; Yu,

2010). Notably, the majority of previous studies have used disclosure quantity as a

proxy for disclosure quality, assuming that disclosing larger amounts of information

leads to a higher level of information quality. However, these studies - especially those


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conducted outside the US - have not provided evidence on the factors determining

disclosure quality regardless of quantity. Similarly, there is a lack of evidence on the

separate influence of each of them on stock market participants.

Despite the importance of KPI information, it appears that there are a limited number of

studies which have looked at KPI reporting (e.g. Hussainey and Walker, 2006; Giunta

et al., 2008; Tauringana and Mangena, 2009; Dorestani and Rezaee, 2011a; Dorestani

and Rezaee, 2011b). The closest studies to the current research are those of Giunta et al.

(2008) and Tauringana and Mangena (2009). Giunta et al. (2008) showed the low level

of quality in terms of financial KPI reporting on the part of Italian firms. Tauringana

and Mangena (2009) suggested that the introduction of business reviews affects

positively the amount of KPIs disclosed by the UK media sector.

To the best of the author’s knowledge, there has been no study that has explored KPI

reporting quantity and quality and has shown variations among firms in practice.

Moreover, previous studies have not examined how KPI reporting quality could

influence a firm’s value. The regulatory environment in the UK offers an interesting

context in which to conduct this study. The regulations enable corporate directors to

control KPI reporting. In addition, the stock market in the UK is developed, and there

should be enough informed financial statement users able to comprehend KPI

disclosures. Overall, this research will contribute to the disclosure literature and could

have policy implications for the UK.

This chapter is structured as follows. Section 1.2 illustrates the nature of this study.

Section 1.3 outlines the motivations for conducting this study. Research questions are

presented in section 1.4. The objectives of this research, in addition to its potential

contribution, are discussed in section 1.5. Finally, details of the organisation of this

study are provided in section 1.6.


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1.2 The nature of the study

In attempting to distinguish between KPI disclosure quantity and disclosure quality, this

study will add to the narrative disclosure literature. KPI information should be of a high

quality if this information is to be useful for the users of annual reports. Therefore, the

starting point of this study has been to assess KPI reporting quality separate from its

quantity. Using different proxies to measure disclosure quality, such as disclosure

quantity or earnings quality, fails to reflect accurate changes in disclosure quality

(Berger, 2011). For instance, stock market liquidity as a proxy of disclosure quality may

capture any changes that are related to the market rather than financial reporting quality

(Berger, 2011). Thus, it was argued that disclosure quality measures should be

comprehensive, and they should consider more than one dimension of disclosure (e.g.

Beattie et al., 2004). Thus, researchers have attempted to assess disclosure quality using

self-constructed indices (e.g. Beretta and Bozzolan, 2004; Boesso and Kumar, 2007).

However, most of the previous attempts have been criticised due to the lack of a

convincing theoretical background (Botosan, 2004), which lead to increased

subjectivity in terms of measurement.

Beyer et al. (2010) reviewed the previous literature in this area and argued that

researchers have to build their disclosure measure based on sensible economic

definitions. Therefore, this study identifies disclosure quality in accordance with the

principal objective of regulatory bodies’ frameworks which is information usefulness.

KPI information quality is measured by the extent to which it is of value to

stakeholders. Consequently, quality scores are produced based upon firms’ alignment

with the ASB (2006) guidance with regard to KPI reporting.

A research instrument is then developed to measure the quantity and evaluate the

quality of KPI disclosure. The quantity of KPI disclosure is measured by counting the


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number of KPIs disclosed in the annual reports of the FTSE 350 non-financial UK

companies under consideration. Manual content analysis is employed to quantify KPI

disclosures for the sample firms.

The study uses the resultant KPI reporting quantity and quality scores to explore the

variation among UK firms with regard to KPI disclosure in practice. Then, these scores

are used to examine the determinants and consequences of KPI reporting.

Most published papers on the determinants and consequences of narrative disclosure

have not distinguished between both dimensions of disclosure: quantity and quality

(e.g. Li et al., 2008; Tauringana and Mangena, 2009; Hassan et al., 2009; Hussainey

and Al-Najjar, 2011; Wang and Hussainey, 2013). Instead, accounting research usually

employs disclosure quantity as a proxy for disclosure quality (e.g. Hussainey and

Mouselli, 2010; Mouselli et al., 2012). According to this approach, it is assumed that

the quantity of disclosure is the same as its quality, and both are derived from the same

factors and have identical consequences.

The distinction between quantity and quality of disclosure allows the present study to

participate actively in the development of empirical disclosure studies. More

specifically, it enables the researcher to investigate the factors affecting each

dimension. In turn, the study tests the separate influence of KPI reporting quantity and

quality on firm value.

1.3 Motivations

Regulators usually ask for an enhancement in the level and quality of information

disclosed by companies. Edward Davey, Minister for Employment Relations, Consumer

and Postal Affairs in the UK stated that:

‘Our goal must be to ensure that our companies are clear-sighted and focused on the

issues which matter to their long term success and therefore to their members.


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Disclosing good quality and relevant information on these issues in company narrative

reporting is necessary if shareholders are to make well informed decisions in their role

as company owners’ (Department for Business, Innovation and Skills , 2010: p.5).

Therefore, from the policy makers’ perspectives, there is a need to be aware of

companies’ current practices with regard to the extent of the information disclosed, and

its quality. In spite of the richness of KPI disclosure content, there is no study that

provides a full picture of KPI reporting practices on the part of UK firms. The closest

studies to this research either look only at the level of one type of KPI disclosure (i.e.

Giunta et al.’s study of financial KPI in Italy (2008)), or focus only on the quantity of

KPI disclosure for one sector and its determinants (i.e. Tauringana and Mangena’s

study of KPI in the UK media sector (2009)). Therefore, the first motivation for

conducting this study is to address this research gap by looking at the characteristics of

KPI reporting. Rather than studying KPI reporting quantity in one sector (Tauringana

and Mangena, 2009), the study shows how UK firms vary in terms of the quantity and

quality of KPI reporting and its subcategories (both financial and non-financial KPIs).

Arguably, this also highlights some areas that could improve in terms of the

communication between companies and information users.

To measure KPI reporting quality, the study has to respond to the call of many scholars

to search for a comprehensive and sensible measure of disclosure in accounting studies

(e.g. Beattie et al., 2004; Beyer et al., 2010). Previous studies use measures of

disclosure quality which have been criticised with regard to their capability to capture

disclosure quality accurately.1 This study addresses this issue by identifying

information quality as a function of meeting the main qualitative attributes that should

1 See section 2.2.2 for more details about measuring disclosure quality in previous literature.


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make this information useful to the users, according to the regulators. The measure

applied in this study is derived from the ASB’s (2006) guidance for best practice in

terms of KPI reporting.

The second motivation is that the UK offers an interesting context in which to examine

the determinants of corporate KPI reporting. CA (2006) provisions imply that KPI

reporting is likely to be voluntary for several reasons: (1) there is no definite set of KPIs

to be disclosed by each firm stating what is a financial KPI and what is not, (2) firms’

directors can control KPI disclosure by identifying ‘the extent necessary’ of financial

and non-financial KPIs for understanding a firm’s performance and its progress, (3)

firms’ directors who are deciding what is ‘appropriate’, when analysing firm

performance and development, may use other KPIs related to environmental and

employee aspects, and (4) firms’ directors can limit the number KPIs disclosed for

competition reasons in accordance with section 417 of CA (2006). Previous research

has examined the determinants of voluntary disclosure (e.g. Li et al., 2008; Hussainey

and Al-Najjar 2011; Wang and Hussainey, 2013).

A limited number of studies have examined the determinants of KPI reporting. The

closest studies to this study have either limited their analyses to KPI disclosure quantity,

e.g. Tauringana and Mangena (2009) - a study of KPI reporting in the UK media sector

- or have ignored the majority of CG variables, e.g. Boesso and Kumar (2007) - a study

for voluntary disclosure practices on the part of US and Italian companies.

This study addresses this gap in the literature by testing the effect of numerous CG

mechanisms, as well as other firm characteristics on KPI reporting. In this regard, the

UK context would have other effects on the analyses, since firms are not required to

apply a specific structure of CG mechanisms in accordance with the ‘comply or


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explain’ rule. Finally, these analyses would provide evidence on whether the quantity

and quality of such narrative reporting are derived from the same factors.

The third motivation for this study arises from the need to examine to what extent KPI

reporting quantity and quality could affect stock market participants. More specifically,

the study investigates whether reporting quantity and its quality have different effects

on firms’ value. As mentioned above, KPI information involves different categories of

information that might be attractive to annual report users. It incorporates important

information that refers to current and future firm performance linked with firm strategy.

Consequently, such information could be valuable when it comes to assessing current

performance, as well as a firm’s ability to pursue its strategic objectives successfully.

Although, previous research has shown the impact of different types of narrative

reporting in different contexts (e.g. Schleicher et al., 2007; Hussainey and Walker,

2009; Hassan et al., 2009; Sievers et al., 2013; Dorestani and Rezaee, 2011a), these

studies have not provided strong evidence on the potential impact. One of the main

explanations for the mixed results in previous research is the lack of a comprehensive

and objective measure of disclosure quality (Beattie et al., 2004). Given the unique

characteristics of the KPI information illustrated above, it is of interest to use the

generated quality scores in order to examine the effects of KPI reporting. This would

add to the literature, especially in the UK, as there is no study that examines the value

relevance of KPI reporting in terms of quantity and quality.

1.4 Research questions

This thesis examines the following research questions:

Q1. What are the main features of KPI reporting in the UK?


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To provide an answer to this question, previous literature with regard to disclosure

measurement is reviewed. Then, an instrument is developed in order to measure KPI

reporting quantity and quality in annual reports. Manual content analysis is employed to

code the text and to classify the KPIs disclosed into financial KPIs and non-financial

KPIs. Quantity and quality scores for a sample of FTSE 350 non-financial UK firms, is

analysed. Descriptive statistics are used to explore the main features of KPI reporting in

the case of UK firms in terms of KPI reporting, including its subcategories. In addition,

descriptive results are used to show changes in KPI reporting across different industries,

and to illustrate these changes across the period (2006-2010).

Q2. What are the factors affecting the level of quantity and quality of KPI reporting in

the UK?

To provide an answer to this question, the study reviews relevant theories that explain

directors’ incentives to control corporate disclosure. Then, drawing on these theories,

the determinants of KPI reporting in terms of quantity and quality are proposed. In

addition to firm characteristics variables, the proposed explanatory variables are

grouped into five subcategories: 1) Directors’ compensation, 2) Board characteristics,

3) Audit committee characteristics, 4) Ownership structure variables, and 5) Capital

need variables. Panel data regressions are conducted to assess the significance of the

association between those variables and KPI reporting in terms of quantity and quality

scores.

Q3. What is the impact of KPI reporting in terms of quantity and quality on firm value?

To provide an answer to this question, the relevant literature is reviewed to explain how

- in theory - KPI reporting could affect firm value. Following previous studies, the


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study controls for firm characteristics as well as growth opportunities. Additionally, the

explanatory variables included in the analyses are: 1) KPI reporting in terms of quantity

and quality scores, 2) Directors’ compensation, 3) Board characteristics, 4) Audit

committee characteristics, 5) Ownership structure variables. Tobin’s Q ratio is used in

the main, and further analyses are used as a measure of firm value. Moreover, tests are

re-estimated using market-to-book ratio as a proxy for firm value to check the

robustness of the results. Panel data regressions are also conducted to test the

hypotheses of this study.

Q4. Can KPI reporting quantity be used as a proxy for KPI reporting quality?

The results of the above three studies are integrated to provide an answer to this

question. The distinction between disclosure quantity and its quality is reached through

the design of the research instrument. Descriptive statistics are used to obtain an

indication of the relationship between KPI reporting in terms of quantity and quality.

Regression results in the second study are employed to show whether each of quantity

and quality in terms of KPI reporting is identically derived from the same factors.

Finally, the findings of the third study are used in order to examine whether quantity

and quality in terms of KPI reporting have different effects on firm valuation.

1.5 Research objectives and contributions

This section illustrates the objectives of this research based upon the above research

questions. Then, the resulting possible contribution to the literature is presented.

1.5.1 Research objectives

Complementary research objectives are set to provide answers to the above research

questions. This research aims to make a contribution to the extant literature.


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Accordingly, this research focuses on UK firms’ practices with respect to an important

type of narrative disclosure (i.e. KPI information). Research findings would inform

academics in depth whether or not the quantity and quality of disclosure are derived

from the same factors. Moreover, the findings would indicate whether or not KPI

reporting has an influence on market participants.

By achieving the following research objectives, the findings of the research would be of

interest to regulators, firms and shareholders.

1. To provide a proper measure for KPI reporting in terms of quality and quantity.

2. To explore the main features of KPI reporting in the UK.

3. To identify the determinants of KPI reporting in terms of quantity and quality.

4. To investigate the impact of KPI reporting in terms of quantity and quality upon

firm value.

5. To examine the extent to which KPI reporting in terms of quantity can operate

as a proxy for KPI reporting in terms of quality.

1.5.2 Research contributions

To the best of the author’s knowledge, there is no recent academic study that has looked

at the level of quantity or quality in terms of KPI reporting among a sample of UK

listed companies. Additionally, there is no study that has examined either the

determinants or economic consequences of KPI reporting in the UK, distinguishing

between disclosure quantity and quality.


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Several novel contributions to the literature are made by this study. The first substantial

contribution is that the study attempts to explicitly differentiate between the amount of

KPI disclosure and its quality. This provides the opportunity to study each dimension in

practice. Subsequently, this distinction helps to obtain greater insights into the drivers

and implications of KPI reporting.

Hence, the study develops a valid and reliable measure of disclosure quality. This

measure builds on the view that narrative reporting should provide useful information to

different users. As mentioned above, the disclosure quality measure is based upon the

ASB guidance for best practice.

Arguably, this measure of disclosure quality offers many advantages: (1) the measure is

based upon a framework of a well-recognised regulatory body (i.e. ASB, 2006) that

aims at information usefulness, (2) the study maintains consistency in evaluating the

quality of KPI reporting for UK firms, since it uses the KPI disclosure guidance that is

recommended to be followed by UK firms, (3) the measure focuses on the qualitative

attributes of the information disclosed, so it would be relevant to measure the quality of

any type of narrative disclosure (e.g. risk reporting) which would provide insights into

disclosure studies in the future, (4) since the dimensions used as a basis for evaluating

disclosure quality are clear, the measure does not require a wide degree of subjective

judgment on the part of the coder. Hence, the disclosure quality measure does not suffer

from high subjectivity which is a common weakness of self-constructed indices

employed in previous research, (5) the ability to ensure the reliability and validity of the

measure is due to the use of many procedures, and (6) having distinct disclosure quality

and quantity scores helps to examine whether or not the two dimensions can operate as

substitutes.


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The second major contribution of the study lies in exploring KPI reporting practices for

a sample of UK companies. If compared with Tauringana and Mangena (2009), the

study presents a holistic view in terms of the overall level of KPI, including financial

KPI and non-financial KPI as disclosed by UK listed companies from different sectors

over a five year period. Furthermore, it is the first academic study - to the best of the

author’s knowledge - that examines whether or not UK firms are keen to introduce high

quality KPIs in their annual reports. The study also shows to what extent KPI reporting

quantity and quality varies across different industries over the sample period (2006-

2010). In particular, the results are expected to be of interest to UK regulators. They

should offer clear guidance for each industry that identifies a minimum and specific

number of KPIs to be issued. The guidance should provide the definitions and the

assumptions used to drive each of these KPIs. This avoids the lack of comparability that

might exist between firms in the same sector.

The third contribution of this study is that the thesis explores the factors affecting KPI

reporting in terms of quantity and quality. Drawing on agency theory, signalling theory,

capital need theory, political need theory, stakeholder theory and information cost

theory, this study highlights the role played by CG mechanisms in affecting the quantity

and quality of KPI reporting. In particular, the study contributes to the literature about

the association between directors’ compensation and corporate disclosure. Moreover,

this study provides evidence that the quantity and quality of KPI disclosures are not

identically determined by the same factors. These findings are of interest to regulators

who are working on enhancing KPI reporting in particular and narrative disclosure in

general.

Another contribution is made to the literature by examining the value relevance of KPI

reporting. To the best of the author’s knowledge, this is the first study to test whether


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or not KPI reporting in terms of quantity and quality have any and different impact on

firm value. The study findings show that the quantity and quality of disclosure have

different influences on firm value. Because of its potential negative effect on firm

value; the results of the study alert firms to the consequences of excessive KPI

disclosure. However, the study finds that disclosure quality has no significant

association with the value of UK firms. Researchers need to consider this finding if they

are going to examine narrative disclosure (or certain types of disclosure) in terms of its

impact on stock market participants. On the other hand, the findings indicate that UK

investors do not enhance the valuation of firms as a result of most CG mechanisms.

This could be of interest to regulators, suggesting that imposing a certain CG structure

on UK firms might not be justified with regard to valuation considerations.

Finally, this research explores the question as to whether or not the quantity of

disclosure can be used as a measure of its quality. As discussed above, the study makes

a distinction between each dimension. The study findings suggest that disclosure

quantity and disclosure quality should not be used as substitutes in accounting research.

Each of them could be derived from different determinants, and might lead to different

consequences. Consequently, researchers should consider this finding with regard to

related studies in the future. This could also contribute to the literature by generating

more research opportunities that could validate previous research findings in many

areas (e.g. factors affecting disclosure levels, the impact of financial reporting).

1.6 Organisation of the study

The structure of this thesis indicates that there are three chapters which deal with three

main studies with regard to KPI reporting. Each of these chapters contains a review of


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the relevant literature. Hence, there is no need for an additional chapter for a literature

review or relating to theory or methodology. Thus the thesis is organised as follows:

Chapter (2) provides an answer to research question 1. It introduces a measure for KPI

reporting quality based upon the ASB (2006) framework. Then, quantity and quality

scores are analysed in order to explain the main features of KPI reporting practices in

the UK. Furthermore, descriptive analyses explain the change in KPI reporting over

time and across industries.

Chapter (3) examines the factors affecting the level of quantity and quality of KPI

reporting in the UK, and hence provides an answer to research question 2. The chapter

includes the theoretical basis for explaining the managerial incentives to control

corporate disclosure, as well as identifying factors affecting such disclosure. The

findings of the analyses also help to assess the validity of using quantity of disclosure as

a proxy for quality in accounting studies, and hence provide an answer to question 4.

Chapter (4) provides an answer to research question 3. It investigates whether or not the

quantity and quality of KPI reporting have any or a different influence on firm

valuation. Furthermore, analyses show how financial and non-financial KPI reporting

could have a different impact on firm value. Finally, this chapter links the findings to

question the validity of using quantity of disclosure as a proxy for quality in previous

research, and hence provides an answer to research question 4.

Chapter (5) provides the concluding remarks of this thesis. It provides a summary of the

research objectives, questions, and the approach followed. In addition, it presents a

summary of the key findings of the research and discusses their implications. The


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remaining part of the chapter shows a summary of the limitations of this research, and

highlights several avenues for future research.

CHAPTER TWO: KPI REPORTING IN THE UK: DESCRIPTIVE STATISTICS

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Chapter 2 - KPI reporting in the UK: Descriptive statistics

2.1 Overview

The main objective of this chapter is to provide an answer to the first research question

(Q1). It explores the main features of KPI reporting in the UK. The present study is

distinguished by exploring the level of quantity as well as of quality in terms of KPI

reporting for a sample of UK listed companies from different sectors over a five year

period.

The quantity of KPI disclosure is measured by counting the number of KPIs disclosed

in the annual reports. On the other hand, the study builds on, and contributes to, the

literature that focused on the qualitative attributes of the information disclosed (e.g.

Beattie et al., 2004; Beretta and Bozzolan, 2004; Giunta et al., 2008; Beest and Braam,

2011). Thus, the study introduces a measure for KPI reporting in terms of quality, based

upon the well-recognised regulatory framework in the UK. Hence, KPI reporting in

terms of quality scores for firms are identified, based upon the sample firms’

compliance with the ASB (2006) guidance for disclosing high quality KPI information.

It is expected that the soft regulations could lead to reporting of KPIs on a voluntary

basis. For instance, directors could take advantage of allowing them to report on KPIs if

they consider them as necessary and appropriate to the analysis of the firm’s

performance. They also could avoid reporting on KPIs when they consider such

disclosure harmful or against the competitive position. Thus, one can expect that

companies would vary in terms of the quantity of the KPIs disclosed or their quality.

Quantity and quality scores are analysed with the use of descriptive statistics in order to

explain the main characteristics of KPI reporting in the UK. Furthermore, descriptive

analyses explain the changes in KPI reporting over time and across industries. The


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analyses are extended to show the corresponding descriptive results for the quantity and

quality of financial and non-financial KPI reporting.

Measuring KPI reporting is essential in order to proceed with answering the remaining

research questions. Based upon these measures, the following chapters investigate the

determinants and consequences of KPI reporting, distinguishing between disclosure

quantity and quality. Consequently, it becomes feasible to examine whether or not

reporting quantity can be used as a proxy for its quality.

The remainder of this chapter is organised as follows: section 2.2 discusses the

regulatory framework of KPI reporting in the UK, and reviews previous research.

Section 2.3 illustrates the methods used in this study. It shows the steps followed to

construct a measure for KPI reporting. In addition, it presents a pilot study conducted

before starting the main analysis. Pilot study results help to ensure the reliability of the

research instrument. Section 2.4 shows the sample selection process, and introduces the

variables in the remaining chapters of the study. Section 2.5 displays the results of the

study with respect to overall KPI reporting, as well as its subcategories. The findings

provide a full picture of KPI reporting on the part of UK firms. They show how

quantity and quality of KPI disclosures and its subcategories varies across firms in

different industries. In particular, the findings also highlight the low level of quantity

and quality of non-financial KPI reporting provided by the sample firms. Finally, the

discussion and overall conclusion of this study is provided in section 2.6.

2.2 Regulatory framework & literature review

2.2.1 Regulatory framework & previous studies

Neely et al. (1995) demonstrated that performance measurement is a process that

requires measures to quantify the efficiency and effectiveness of actions. Accordingly,


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each organisation needs a set of performance indicators to measure and analyse its

overall performance. As a result, every company has to identify the primary

performance indicators that have a significant impact upon its current and future

success. These key performance indicators (KPIs) could increase a firm’s performance

dramatically, by affecting more than one of the critical success factors as they apply to

the firm (Parmenter, 2010).

Section 417 of the Companies Act (CA) defines KPIs as: “…factors by reference to

which the development, performance or position of the business of the company can be

measured effectively” (CA, 2006, p.8). Whereas it was argued that KPIs inclusively

represent a set of non-financial measures (Parmenter, 2010), others consider that

financial KPIs is the principal category of firms’ KPIs (Giunta et al., 2008; CA, 2006).

Broadly speaking, a KPI refers to a critical perspective in terms of business

performance (Parmenter, 2010). Based upon the content of each KPI, KPIs can be

classified as financial or non-financial, quantitative or qualitative, historical or forward

looking, and an indicator which contains either good news or bad news (Hussainey and

Walker, 2006; Boesso and Kumar, 2007).

Reporting on KPIs is regarded as the core of the business reporting system (Bray,

2010). It is expected that KPI reporting would be a valuable source of information for

user groups. KPI information contains relevant information related to the strategy of the

company, board objectives, and value creation activities. Arguably, KPI reporting is an

effective means to improving both the transparency and relevance of public financial

information (Dorestani and Rezaee, 2011a). Firms might use this type of disclosure to

support their communications with stakeholders. Hence, KPI reporting could improve

the users’ ability to evaluate a firm’s performance, to assess its position comparing with

that of its competitors, and to offer a broad overview of the firm’s ability to achieve a


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sustainable competitive advantage.

In its report to the U.S. Securities and Exchange Commission (SEC), the Advisory

Committee on Improvements to Financial Reporting (ACIFR, 2008) stated that KPI

reporting might lead to an increase in the usefulness of information for investors. This

increase is expected because KPI disclosures display important aspects of companies’

activities that might not be reflected clearly in the financial statements. Therefore, it is

not surprising that sophisticated users show a higher reliance on quantitative forecasts

of both financial and non-financial KPIs, in the evaluation of the current and future

performance of the business (Pratt and Beattie, 2002).

The importance of KPI reporting encourages many regulatory bodies to require firms to

publish this critical information. For example, the Institute of Chartered Accountants of

Scotland (ICAS) (1999) proposed that improved business reporting should provide

additional information which might be captured by management information system,

such as performance indicators and intellectual capital (Beattie, 1999). The EU

Accounts Modernisation Directive (2003) required that entity’s reporting should

include a business review. This review must contain analysis using KPIs. This

requirement has been adopted by the UK Companies Act (CA) of 2006 (section 417).

Additionally, the International Accounting Standards Board (IASB) issued the IFRS

Practice Statement ‘Management Commentary’ (MC) in December 2010 (IASB, 2010).

This statement presents a broad framework for the preparation and presentation of a

management commentary. The statement stated that such a management commentary

should include information that helps to understand the critical performance indicators

used by management to evaluate the performance against the objectives of the entity. It

seems that this statement responded to the call to make MC matter to the investment


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community by illustrating the value creation structures of the company through a set of

KPIs (Nielsen, 2010).

Regarding KPI reporting in the UK, the directors of quoted companies were first asked

to prepare operating and financial review (OFR). According to Schedule (7ZA) of the

CA (1985), the OFR should include a comprehensive analysis of: 2

(a) The development and performance of the business of the company during the

financial year, and the position of the company at the end of the year,

(b) The main trends and factors underlying the development, performance and position

of the business of the company during the financial year.

(c) The main trends and factors which are likely to affect the company’s future

development, performance and position.

As mentioned earlier, reporting on KPIs is restated in section 417 of the CA (2006)

asking the directors of all companies - except small ones - to analyse the company

performance using KPIs in a business review. The KPIs used in the review should meet

the need of stakeholders when it comes to understanding the position and the

development of the business.

Furthermore, the Accounting Standard Board (ASB) issued the OFR reporting

statement which highlights the role of KPIs as a tool for analysing business

performance through the board of directors. KPI information is required to help in

assessing the firm’s progress in achieving its business strategies (ASB, 2006).

Consequently, OFR (2006) contains guidance concerning the content of KPI disclosure

in such a way as to achieve the best level of usefulness for the users (ASB, 2006).

It is stated that KPIs could be financial or, if appropriate, non-financial to cover

environmental and employee matters (ASB, 2006; CA, 2006). Furthermore, it is

2 Extracted from Department for Business, Innovation and Skills (2010). Available at:

http://w w w .bis.gov.uk/assets/biscore/business-law /docs/n/10-1057-future-narrative-reporting-consultation.pdf


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recommended that KPIs should be widely used within the industry or the sector for

comparability purposes.

With respect to professional bodies, many surveys have been conducted in order to

explore companies’ reporting practices after the introduction of the business review

(Deloitte, 2006; PriceWaterHouseCoopers, 2006; Deloitte, 2009. For example, Deloitte

(2006) reviewed the annual reports of 100 listed firms which were published in the

period from 1 August 2005 to 31 July 2006. It was found that 45% of companies

presented KPIs. The average number of KPIs was six, with the average number of non-

financial KPIs being two.

In November 2006, PricewaterhouseCoopers (PwC) analysed 128 annual reports. The

results showed that companies responded to the OFR guidance regarding KPIs. An

increasing number of companies used KPIs as a tool to assess performance (32% at

March 2005 year-end compared with 75% of companies as of March 2006 year-end).

However, the majority of reported KPIs were financial.

The ASB (2007) conducted a review of companies’ narrative reporting practices. The

main conclusion of this review was that the lack of non-financial KPIs might be due to

the difficulty of disclosure on this category for companies. Tauringana and Mangena

(2009) explained that by the companies’ tendency to take advantage of exemption

provisions 10 and 11 in section 417 under the CA (2006). These provisions allow some

UK firms not to disclose this type of information for confidentiality reasons. Hence,

these companies considered the release of KPI information to be seriously prejudicial to

the company’s interests.

In 2009, Deloitte examined the narrative sections contained within the annual reports of

130 listed companies, including 30 investment trusts. It was reported that 84% of

companies (77% in 2008) clearly identified their KPIs. The average number of KPIs per


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company was eight, of which five were financial in nature and three were non-financial.

However, companies’ performance in explaining the KPIs selected, and their link with

strategy, was relatively poor. Many companies did not provide sufficient information

for the reader to understand the reasons for selecting certain KPIs. Finally, the study by

Deloitte indicated that the top three most common KPIs disclosed were profitability,

shareholder return and employee-related measures.

More recently, the Financial Reporting Council (FRC) reviewed the narrative reporting

on the part of UK listed companies in 2008/2009 (ASB, 2009). This review indicated

that some companies might not consider KPI disclosure necessary or appropriate for

understanding the development and performance of the business. However, companies

have started to improve KPI communication by using graphical illustrations and tables.

On the other hand, there are a limited number of academic studies that have

investigated KPI reporting in annual reports. Hussainey and Walker (2006) explored

analysts’ reports to investigate whether or not they rely on KPI disclosure. While their

study gave a good indication of analysts’ usage of different KPIs among high and low

growth companies, it did not investigate the characteristics of KPI reporting in the UK.

In addition, the study used a small sample of analysts’ reports that were concerned with

two types of UK companies (high and low growth companies).

Tauringana and Mangena (2009) examined the extent of KPI reporting and the factors

affecting its level, before and after the introduction of the business review. They

employed content analysis on the annual reports of 32 media sector companies listed on

the London Stock Exchange over a four year period (2004-2007). Their findings

suggested that the introduction of the business review had a significant impact upon

KPI reporting in the media sector. In addition, the authors showed that as late as 2007,

25% of companies were still not reporting any KPIs. Besides that, they highlighted the


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association between the extent of KPI reporting in media companies and the proportion

of Non-Executive Directors (NED), company size, profitability and gearing.

Tauringana and Mangena (2009) provided evidence on the change in KPI practices in

UK media sector firms. It also indicated several factors that might affect the level of

KPIs disclosed. However, the study focused on media companies exclusively, with a

small sample size, which makes generalisation of their findings difficult. Furthermore,

the study did not investigate the quality of KPI disclosure.

Looking at KPI reporting studies outside the UK, Giunta et al. (2008) explored the

quantity and quality of KPI reporting in the Italian context. Their study focused on

Italian companies’ practices regarding financial KPIs published in the annual reports

over the period 2004-2006. While they measured KPI quantity based upon the number

of financial KPIs published, disclosure quality was measured based on the presence/

absence of 10 qualitative aspects. Then, these aspects were grouped according to the

four general dimensions introduced by the IASB (2005); relevance, understandability,

reliability and comparability. Then, quality scores were derived by calculating the mean

among the four dimensions. The study results showed the low level of financial KPIs in

terms of extent and quality in Italy, supporting their call for a regulation with regard to

narrative disclosure in MC in general, and in KPI reporting in particular.

While Giunta et al.’s (2008) study was the first to look at the quality dimension of KPI

disclosure, the study relies on a relatively small sample comprising medium size

companies in the Italian setting. Moreover, the analyses are limited to one type of KPI

reporting in the form of financial KPI reporting.

Finally, other studies raised questions about the impact of KPI reporting. Dorestani and

Rezaee (2011a) examined the association between non-financial KPI disclosure and the

accuracy of analysts’ forecasts for a sample of US firms for the two-year period


25

between 2006 and 2007. Their results suggested that the change in KPI quantity

(measured by the ratio of the total number of KPI keywords disclosed to total words

included in the management discussion and analysis) does not have strong impact on

the accuracy of analysts’ forecasts.

Using the same definition of the extent of KPI reporting, Dorestani and Rezaee (2011b)

examined whether or not investors’ perceptions about the quality of earnings are

associated with the quantity of non-financial KPIs disclosed by US firms across the

period 2006-2007. They found a positive relationship between the extent of non-

financial KPIs disclosed and earning quality (measured by a factor that captures the

association between current accruals and cash flows). Yet, Dorestani and Rezaee’s

studies laid stress on investigating the impact of non-financial KPI reporting. They

neither included time series analyses of KPI reporting nor explored the practices with

regard to KPI reporting in terms of quantity and quality. Moreover, Dorestani and

Rezaee (2011a; 2011b) did not consider financial KPI reporting in their analyses.

On the other hand, Booker et al. (2011) highlighted the impact of non-financial

performance indicator narratives upon users’ perceptions. The results provided evidence

that non-financial KPI information could influence individuals’ actions, and could

increase their perceptions of the predictive content of these KPIs. However, their

experimental study is one which has limited power to enable generalisation if compared

with empirical studies.

To conclude, despite reporting on KPIs is required in the UK in accordance with the CA

(2006), it is not clear what should be presented as a KPI, or how to distinguish between

KPIs and other performance results. Additionally, there is no identical set of KPIs to be

reported on for all companies.

Arguably, the nature of the requirements implies that company directors have a wide


26

area of discretion when it comes to controlling KPI reporting in several ways:

a) They can determine the ‘the extent necessary’ with regard to financial and non-

financial KPIs when it comes to understanding the development, performance or

position of the business of the company.

b) They can determine what is ‘appropriate’ when undertaking analysis using other

financial key performance indicators, including those related to environmental and

employee aspects, and

c) They can take the advantage of exemption provisions 10 and 11 in section 417 of CA

(2006) to control the extent of KPI information for confidentiality reasons.

In addition to this interesting setting, it is apparent that previous surveys placed

particular stress on exploring the quantity of KPI reporting for small samples of UK

companies, and covered a short period of time. They did not explore KPI reporting in

terms of quality, or provide a systematic guidance to measuring it.

In summary, only a small number of studies have examined the characteristics of KPI

disclosures in general, and in the UK in particular. The current study investigates KPI

reporting on the part of UK firms across different sectors. In addition, this study

addresses not only the quantity but also the quality dimension of KPI reporting.

However, measuring KPI reporting quality is one of the key challenges in the current

study. Before introducing the measure adopted by the study to evaluate KPI reporting

quality in annual reports, the next section will start by reviewing the disclosure

literature that has focused on measuring reporting quality. Consequently, section 2.3

shows all procedures followed to develop a reliable and valid instrument used to

measure KPI reporting quality.


27

2.2.2 Measuring KPI reporting quality

In the first phase of their recent joint framework, the International Accounting

Standards Board (IASB) and the Financial Accounting Standards Board (FASB) in the

U.S. emphasised that the main objective of financial reporting is to provide existing and

potential users with useful accounting information (IASB, 2010). Thus, providing

information of a high quality is important for those users to help them in decision

making. However, there is a great debate about the definition and measurement of

disclosure quality (Beretta and Bozzolan, 2004; Botosan, 1997; Beest and Braam, 2011;

Anis et al., 2012).

Beyer et al. (2010) reviewed the disclosure literature and concluded that the authors

have missed the economic definition of disclosure quality. Hence, there is a lack of a

measure that is directly derived from this definition. Botosan (2004) stated that the

conceptual frameworks of accounting bodies provide the guidance to set out generally

accepted notions of information quality. In line with this suggestion, the current study

uses the main qualitative characteristics of the information disclosed as the foundation

for the concept of disclosure quality.3

Thus, KPI disclosure quality represents the extent to which KPI information can

provide useful information to different stakeholders. This information should be

relevant, comparable, reliable and understandable so as to help them in making

decisions. Accordingly, KPI information that is characterised with such attributes would

lead to a better understanding of the development and performance of the business

during the financial year, an evaluation of the current position of the company with

3 It is worth mentioning that Giunta et al. (2008) used the ASB (2006) framework - the same as is used in

the current study - to develop their disclosure quality measure. However, they added two other attributes

suggested in the OFR (2006). However, adding these attributes is not justified as they are not based upon

ASB guidance or even on the IASB framework. One can argue that this could affect th e validity of their

disclosure measure.


28

regard to its competitors, and an assessment of the progress of the board in achieving

business strategies. This the definition of KPI reporting quality explicitly reflects the

principal objective of the major accountancy and regulatory bodies such as the

American Institute of Certified Public Accountants (AICPA) and the ASB (AICPA,

2006; ASB, 2006).4 Furthermore, the above definition highlights the characteristics that

are essential, when it comes to developing a measure to assess the quality of KPI

disclosure in particular.

With respect to difficulty in measuring disclosure quality, there are many approaches

that have been followed in previous studies (see Healy and Palepu, 2001; Hussainey,

2004; Hassan and Marston, 2010; Beyer et al., 2010). Numerous studies used the

quantity of disclosure as a proxy for its quality (Hail, 2002). For instance, Hussainey

and Mouselli (2010) used the number of future-oriented earning statements as a proxy

for disclosure quality. However, providing more disclosure is not an indication of the

quality of the information disclosed. In addition, high reporting quantity that belongs to

a specific type of disclosure (e.g. forward looking earning statements) might not

necessarily indicate high or low reporting quality for the other types of information

disclosed (Anis et al., 2012).

Some studies have considered earnings or accruals quality as measures of disclosure

quality. For example, Dechow and Dichev (2002) modelled the relationship between

working capital accruals and operating cash flows in order to evaluate earning quality.

Hence, financial reporting quality should include the quality of both financial

information and non-financial information (Beest and Braam, 2011). In their review of

the literature, Beyer et al. (2010) illustrated that future studies should take into account

that earning or accruals quality might not be a valid measure that captures the variation

4 It is argued that the OFR issued draws upon the Jenkins framework issued by the AICPA in 1994

(Beattie et al., 2004).


29

in accounting information quality.

Assuming that auditors usually ask for higher fees from firms with lower reporting

quality, Hirbar et al. (2010) found that unexplained audit fees could work as a proxy for

disclosure quality. They found that this proxy is more powerful in predicting fraud and

restatement, and hence offers a better measure of the quality of earnings or accruals.

Since it could capture the extent of auditor’s independence rather than earnings’ quality

(Berger, 2011), this proxy is still imprecise when it comes to explaining the variation in

disclosure quality.

Rogers (2008) presented a different proxy for disclosure quality. He depended on the

underlying positive association between disclosure quality and market liquidity, to

justify using the change in market liquidity as a proxy for disclosure quality. However,

this proxy - like other proxies that follow the same approach - suffers from a limitation:

changes in market liquidity (or any proxy that relies on market measures) might be

influenced by other factors rather than by disclosure quality (Berger, 2011).

Healy and Palepu (2001) referred to three other common proxies used to measure

disclosure quality in previous studies: management forecasts, subjective ratings, and

self-constructed indices.

Management forecasts consist of forward-looking information voluntarily provided by

management. Management forecasts are usually used by U.S. researchers thanks to their

availability in the First Call database and in the Dow Jones News Retrieval Service

(Hassan and Marston, 2010). Management earnings forecasts can be verified through

actual earnings realisations (Hassan and Marston, 2010). However, these forecasts are

only one component of managers’ voluntary disclosure package, and hence, it is not

sensible to use this type of information only as a proxy for the overall level of corporate

disclosure quality (Hussainey, 2004).


30

As far as subjective ratings are concerned, these ratings are comprehensive measures of

the overall level of corporate disclosure quality.5 The most common example are the

surveys conducted by the Financial Analysts Federation (FAF) / the Association for

Investment Management and Research (AIMR) which have been used as proxies for

disclosure quantity and quality in several previous studies (e.g. Lang and Lundholm,

1996; Botosan and Plumlee, 2002).

The FAF and AIMR reflect the ratings given by leading financial analysts for

mandatory and voluntary disclosure made by companies. Although disclosure quality is

assessed comprehensively through the use of experienced experts, the ratings could

suffer from some limitations (Healy and Palepu, 2001; Hussainey, 2004). In particular,

analysts show - to some extent - subjectivity and bias by just including large US firms

in the ratings (Healy and Palepu, 2001), or by giving higher ratings to firms with better

current and expected operating results (Lang and Lundholm, 1993). In addition, AIMR-

FAF ratings cannot be used any longer as they were discontinued in 1997, with the last

year of the disclosure scores being 1995.

Hussainey (2004) showed some other subjective ratings that have been used in previous

studies as proxies for the quality of corporate disclosures. These ratings include

Financial Post ratings; Australian Stock Exchange ratings; SEC ratings; Society of

Management Accountants of Canada (SMAC) ratings and the Center for International

Financial Analysis and Research (CIFAR) ratings. However, these ratings basically

depend on analysts’ and accountants’ opinions with regard to firm’s disclosures

(Hussainey, 2004). Therefore, the inherent subjectivity in these ratings does not allow

using them widely in accounting studies.

5 For more detail see: Healy and Palepu (2001); Hussainey (2004); Hassan and Marston (2010); and

Beyer et al. (2010).


31

As indicated above, using a proxy that indirectly reflects reporting quality levels is

problematic. Therefore, self-constructed disclosure indices have been introduced by

researchers to measure disclosure quality. In particular, their attempts have been aimed

at developing a measure that captures the qualitative characteristics of information

which could improve its usefulness. This multi-dimension approach - to assess

disclosure quality – has been adopted in several studies (e.g. Beattie et al., 2004; Beretta

and Bozzolan, 2004; Beest and Braam, 2011; Anis et al., 2012). For example, Beattie et

al. (2004) take into account three types of information attributes: financial/non-

financial, quantitative/ qualitative, and historical/forward looking. According to this

approach, the researcher searches the text and produces a disclosure score determined

by the presence or absence of qualitative attributes in the disclosed information. Finally,

total scores are derived as a result of aggregating the individual score for each piece of

information. In some cases, weighted scores are produced to highlight the importance of

some dimensions.

However, the studies which have adopted this approach exhibit different

problemsBeattie et al. (2004), as well as Beretta and Bozzolan (2004), used measures of

quality that relied – to some extent - on disclosure quantity. Additionally, the measures

introduced did not define disclosure quality or its dimensions in accordance with any of

the regulatory frameworks. Regulatory frameworks present the qualitative

characteristics needed to make financial reporting more useful (Botosan, 2004). Thus, it

could be argued that it is better to assess firms’ reporting qualities in accordance with

the frameworks of regulatory bodies.

On the other hand, other studies which have considered the regulators’ perspective with

regard to quality definition have revealed different limitations. For example, when

Beest and Braam (2011) constructed their index, they considered the qualitative


32

attributes of information illustrated in the IASB (2008) exposure draft. Their

comprehensive measure gave the opportunity to make subjective interpretations on the

part of the coders which affected the reliability of the measure. Moreover, the authors

acknowledged that they could not ensure the validity of the measure. Anis et al. (2012)

defined and developed their measure of reporting quality in accordance with the OFR

guidance (2006) framework. Despite using software to code the text over a relatively

large sample6, Anis et al.’s (2012) measure is unclear, and might suffer from double

counting. For instance, they used the quantity of forward-looking information to reflect

on three of the eight dimensions that are aggregated to measure disclosure quality (i.e.

the forward-looking orientation, verifiability, and relevance dimensions). Moreover, their

measure is too general to reflect reporting quality over the whole annual report. For

example, they used the presence of the quantitative KPI section to assign a

comparability dimension score. Whereas it might be considered as an indication of KPI

reporting quality in the report, it cannot be generalised to the whole report. A firm can

provide a quantitative KPI section and ignore the comparability dimension in all other

types of disclosure throughout the report.

Despite the limited number of studies that analysed KPI reporting, two studies used a

self-constructed index to measure the quality of KPI disclosures (i.e. Boesso and

Kumar, 2007; Giunta et al., 2008). Boesso and Kumar (2007) compared the drivers of

voluntary disclosure in terms of KPI information in the US and in Italy. Based on the

previous literature, they employed an aggregated index in which KPI reporting in terms

of quality is a function of the following dimensions: the outlook of the KPIs disclosed

(historical or forward-looking); the type of KPIs reported (quantitative or qualitative);

6 It is worth mentioning that using automated analysis has many limitations if compared with manual

content analysis.


33

and the nature of the KPIs (financial or non financial). They gave double weight to

forward-looking, quantitative, and non-financial KPIs in contrast to historical,

qualitative and financial ones. However, Boesso and Kumar (2007) measure implicitly

mixed between the quantity of disclosure and its quality. Therefore, it can not be used

to assess disclosure quality regardless of its quantity. Furthermore, this quality measure

lacks a clear theoretical foundation that explains the definition of disclosure quality, and

hence does not justify either the different dimensions used or the weights allocated to

different types of KPI disclosure. Nevertheless, it can be argued that other dimensions

should be included in order to assess the reliability and understandability of the KPIs

disclosed. The weights are to a great extent subjective; it will be problematic if a

researcher seeks to compare quality scores for different KPI categories (e.g. financial

KPIs and non- financial KPIs).

Giunta et al. (2008) addressed these issues when they used a self-constructed index to

assess the quality of financial KPIs for a sample of Italian firms. They constructed their

measure in accordance with the common objectives of the main regulatory bodies (i.e.

IASB, Canadian Institute of Chartered Accountants (CICA) and the ASB). They

identified ten qualitative attributes to be included in their index. Then, these attributes

were grouped following the four general dimensions presented by IASB (2005), which

include relevance, understandability, reliability and comparability. Finally, quality

scores were derived by calculating the mean among the four dimensions. Giunta et al.’s

(2008) measure of quality showed a better linkage between the dimensions they used to

assess disclosure quality, and the key qualitative attributes of information. However, it

is implied that Giunta et al. (2008) used the ASB (2006) - as with the current study - but

added two other attributes. First, there was the presence of graphs and tables. This could

create confusion for the coder because it is too close to another attribute (i.e. data


34

trend). Consequently, the authors did not justify the reason behind using binary

calculation for all the attributes except for the data trend. Second, they added a

benchmarking dimension which refers to the presence of comparable peer data or sector

data. Thus, adding this attribute was not based on ASB guidance or even on the IASB

framework. As a result, the validity of their disclosure measures could be affected.

The present study uses a disclosure quality measure which is based upon ASB (2006)

guidance for best practice with regard to KPI reporting. Hence, the measure employed

evaluates the reporting quality taking into consideration the qualitative attributes that

make this information useful. Arguably, using the same attributes as indicated in the

ASB (2006) guidance to generate quality scores is more objective. The measure avoids

any subjectivity that may be caused by adding more attributes and, in turn, increasing

coder bias when it comes to scoring.

Overall, the present study contributes to the literature by being the first to investigate

the characteristics of KPI reporting in a UK setting. Unlike Tauringana and Mangena

(2009) who focused on one sector, the current study investigates KPI reporting by

considering non-financial firms from different sectors. This enables the researcher to

observe the variation among firms in different industries with regard to KPI reporting.

Furthermore, compared with the Giunta et al. (2008) study in an Italian setting, the

present study is the first to explore the quality levels of KPI reporting including

financial and non-financial KPI disclosures. The size of the study sample is relatively

large compared with the most similar studies with a longer time series.7 Therefore, the

study results could be generalised, which could have different implications for users

and interested regulatory bodies. The study also provides many research opportunities

7 However, one common limitation of labour intensive studies is the relatively small sample size. Given

that manual content analysis is employed to code the text, and the majority of CG variables data is hand -

collected by the researcher, sample size is - to some extent - restricted due to time and effort

considerations.


35

based upon that distinction between the quantity and quality in terms of KPI reporting.

For example, researchers could empirically investigate whether or not reporting

quantity and quality are derived from the same factors.

2.3 Methods

There is neither a general definition for KPIs nor a certain set of KPIs to be disclosed

by all UK firms. Therefore it is suggested that manual content analysis could be a more

relevant approach to quantifying KPI disclosures for each firm. This traditional

technique has been used in previous studies (e.g. Beretta and Bozzolan, 2004; Beretta

and Bozzolan, 2008; Linsley and Shrives, 2006; Abraham and Cox, 2007). It helps to

avoid many of the drawbacks of automated content analysis. These drawbacks include

misleading results due to using inappropriate/insufficient key words or using the words

in isolation of the whole meaning of the sentence, in addition to the limitations related

to the software used to perform the analysis (Hassan and Marston, 2010).

KPI reporting in terms of quantity refers to the amount of KPI information in the annual

report. It is measured by the number of KPIs that are published by a firm. KPI

information in the current study is classified into financial and non-financial KPIs.

Financial KPI disclosure includes all information about the key factors that affect the

financial performance of the firm and its development. These KPI could usually be

driven using financial statement items such as cash flow, operating profit, return on

capital employed, research and development expenditure, earnings per share…etc.

Financial KPIs could be helpful to annual report users in terms of evaluating the firm’s

financial performance, and assessing its current competitive position. On the other

hand, non-financial KPI disclosures include all non-financial information about the key

factors that affect the performance of the firm and its development. These KPIs could

cover operational, environmental or employee aspects. Non-financial KPIs are not


36

driven directly by financial statements such as new product launches, emissions,

number of employees, staff attrition rate….etc. Rather than financial KPIs, non-

financial KPIs are concerned with other perspectives of a firm’s performance.

Arguably, financial, together with non-financial KPI information, could enable different

stakeholders to have a full picture about the critical factors that affect the current and

future performance of the firm.

Having distinct disclosure quality and quantity scores helps to examine whether or not

the two dimensions can operate as substitutes. Thus, to measure KPI reporting in terms

of quantity in the study, an un-weighted approach is employed to code and measure KPI

disclosures throughout the annual reports. Therefore, ‘1’ is given for each KPI disclosed

in the annual report. Ahmed and Courtis (1999) claimed that un-weighted scores are to

be preferred due to subjectivity concerns. The current study follows an un-weighted

approach to measure KPI reporting in terms of quantity since there is no theoretical

basis for weighting either financial or non-financial KPIs. Marston and Shrives (1991)

acknowledged the fact that weightings are usually achieved by conducting surveys

among relevant user groups. However, they questioned the rationale behind supposing

that rating an item as a four indicates that this item is four times as important as an item

rated as a one. Moreover, this study is not focusing on one particular user group. Cooke

(1989) argued that weighting would be useless when research is not focused on a

particular user group. He claimed that each group would attach different weightings,

which would result in them eliminating each other’s effects. In consistency with this

view, Firth (1980) observed that weighted and un-weighted scores lead to similar

results.

Overall, each company has been given a score in terms of quantity, which represents the

total number of reported KPIs. These KPIs include both financial KPIs and non-


37

financial ones. For example, looking at Greggs Plc. with regard to its annual reports and

accounts 2010, the company disclosed the following KPIs - financial KPIs: like for like

sales growth, total sales growth, capital expenditure, diluted earnings per share,

operating profit, operating margin. In addition, growth in net shop numbers was

reported as a non-financial KPI. Thus, the quantity of financial (non-financial) KPIs

reporting is six (one). Subsequently, KPI reporting in terms of a quantity score for this

company in 2010 was seven (the total number of KPIs disclosed).

In accordance with the definition of KPI reporting in terms of quality introduced earlier,

the presence of qualitative attributes for each KPI disclosure would enhance the

usefulness of KPI reporting in general. The main advantage of this approach is that of

evaluating reporting quality in a straightforward way by looking at the qualitative

attributes in the disclosed information, rather than inferring disclosure quality by using

a proxy that may not capture changes in quality. Therefore, the study draws upon the

OFR (2006) best practice guidance regarding each KPI’s content. For each KPI, the

guidance states that the following qualitative characteristics need to be considered

(ASB, 2006, p.23, and pp. 29-38):

1- Provision of the definition of the KPI and explanation of its calculation method (e.g.

the average revenue per user ARPU; the number of subscribers, the percentage of

revenue from new products, products in the development pipeline, and customer churn).

2- Explanation of the purpose of adopting a particular KPI (e.g. to assess how the

company is performing in its market; because it is one of the key drivers for future

revenue growth in the industry; to measure and manage the company’s objectives to

increase shareholder value; to reduce churn rate in order to improve revenue).

3- Disclosure of the source of the data (e.g. GAAP financial statements figures; internal

estimates; internal company data).


38

4- Quantify the targets for each KPI (e.g. to achieve a market share of % within X

number of years; to have an economic profit target of £X million).

5- Quantify the data (e.g. disclosure of the corresponding amount for the previous year;

five year trend data; a table of the number and the percentage increase in a KPI from

year to year; a graph showing comparatives and the percentage change year by year).

6- Provision of a commentary on future targets (e.g. the company plans to achieve X

market share in Y segment by the introduction of SS which is a new product or sale

channel).

7- Disclosure of the adjustment for any financial statement information used (e.g.

operating results used for calculating return on capital employed = operating results as

in the financial statements + interest from sales financing).

8- Explanation and disclosure of any changes or of no changes to KPIs (e.g. changes

have been made to the data or calculation methods used).

Arguably, considering these dimensions could result in producing KPIs that possess the

main qualitative characteristics of information as recommended by ASB (2006). It was

recommended that the information provided should be comprehensive and

understandable. Given that KPI information covers many aspects of firm performance,

it should be presented in a way that enables users - with a minimum knowledge of

accounting as well as business activities - to understand this information. Arguably, this

could be achieved if the management presents the definition of each KPI, mentions how

the KPI is calculated, and discloses any changes made to KPIs, explains the purpose of

adopting the KPI by the management, show the trends with regard to each KPI, and

provides a management commentary on the targeted KPI. Furthermore, ASB (2006)

recommended that KPI information should be verifiable. This might be achieved by

disclosing the source of the data used to calculate each KPI, and explaining the


39

assumptions used in calculations, showing any adjustments for any of the financial

statement information used. Moreover, ASB (2006) recommended that the information

disclosed should have forward-looking orientation, so as to assist the user in evaluating

the prospects of the business, as well as management’s plans for achieving its business

strategies. With respect to KPI information, the managers could provide the targets in

for KPIs, and comment on these targets by informing the users how these targets could

be achieved within the stated time frame.

Relevance is an important characteristic of information recommended by the ASB

(2006). Thus, KPI information should be relevant to the users in order to help them to

evaluate a firm’s performance. This could be retained by explaining the purpose of

adopting such a KPI, and indicating that these KPIs could measure the firm’s

performance relative to the firm’s objectives. In addition, a management commentary

on the KPI targets could illustrate to which extent these targets are relevant to managing

future performance. KPI information should also be balanced and neutral. This could be

achieved by quantifying KPI data in such a way as to avoid any bias in the information

disclosed. Hence, users would be informed with regard to the trend in KPI results

showing the change in these results - year by year - regardless of what this change could

mean to the business (i.e. good or bad news). In this regard, comparability is another

important characteristic of information. Users should be enabled to compare KPI

information across different firms, year by year. This could be considered by

quantifying KPI data, explaining the assumptions used in calculating each KPI, and

showing any adjustments for any financial statement information used. Finally, ASB

(2006) stated that disclosed information should be complementary and supplementary

to the financial statements. In accordance with ASB (2006) guidance, KPI information

could provide the user with additional information or explanations of the information


40

included in the financial statements. Thus, one might argue that firms’ intent to explain

or complement information already reported in the financial statement, could lead to the

use of this information to calculate different KPIs. Hence, disclosing whether or not any

adjustments made for financial statement items in order to provide KPI information

would signal the action to complement and supplement financial statement information.

To conclude, firm’s directors should provide KPI information that is relevant, has a

forward-looking orientation, is comprehensive and understandable, is balanced and

neutral, contains complementary and supplementary financial statement, and is

verifiable and comparable over time. In short, it is argued that applying ASB’s would

result in providing KPI information of a high quality.8

Table 1 links the proposed dimensions of KPI reporting in terms of quality with the

main principles introduced by ASB (2006).

Table 1 Overview of the dimensions used to measure KPI reporting quality in

relation to the qualitative characteristics of information

OFR (2006) guidance Linkage to the main

qualitative characteristics

of information

recommended by ASB

(2006)

1- Provision of the definition of the KPI and explanation of its calculation method.

-Comprehensiveness and understandability - Verifiability

2- Explanation of the purpose of adopting the KPI

-Comprehensiveness and understandability - Relevance

3- Disclosure of the source of the data -Verifiability

4- Quantify the targets for each KPI -Forward looking

8 One might argue that the current study avoids disclosure in terms of quantity to be reflected in

evaluating disclosure quality. Therefore, KPI reporting quality measure looks at the extent to which the

mentioned dimensions are maintained by each company. In other words, the study aims at ranking

companies in terms of their compliance with the ASB (2006) guidance. It is observed that companies

vary with respect to the number of KPIs provided. Therefore, using the absolute quality scores would

allow KPI quantity scores to affect quality scores. Thus , the study employs simple averages in order to

produce quality scores. Arguably, this procedure would eliminate the influence of quantity score

differences, and hence, quality scores will not be affected by these differences. Furthermore, the quality

measure does not consider the extent of information with regard to each dimension. This also would

avoid any effect of the quantity of information provided in terms of having an impact on quality scores.

However, it can be claimed that the resultant quality scores might suffer from a limitation. In particular,

quality scores will not take into account the depth of KPI information provided.


41

orientation. -Relevance

5- Quantify the data -Balance and neutrality

-Comprehensiveness and understandability --Comparability

6- Provision of a commentary on future

targets

Forward looking orientation.

Relevance Comprehensiveness and

understandability

7- Disclosure of the adjustment for any financial statement information used

-Comparability -Complementary and supplementary to financial

statements. - Verifiability

8- Explanation and disclosure of any

changes or no changes to KPI

-Comparability

-Comprehensiveness and understandability

It can be argued that the approach used in this study to measure KPI reporting quality

has many advantages. First, the index is based upon a framework of a well recognised

regulatory body (ASB, 2006) that aims at ensuring information usefulness. Second, the

study maintains consistency in designing, coding and measurement processes. The

index employed uses the KPI disclosure guidance which is recommended to be

followed by UK firms. Consequently, this index is specifically used to assess KPI

reporting quality, and hence, it is more convenient when it comes to this type of

information. The index focus is on assessing the quality of information unit, which

makes it valid for being applied to different types of narrative disclosure. For instance,

the index can be applied to measure the quality of risk information disclosed in the

annual report. Finally, the index covers the qualitative attributes of information without

requiring a great deal of subjective judgment. These qualitative attributes are clear

enough to be tracked. Moreover, the binary scoring helps to obtain reporting scores

without the need to make substantial judgments on the part of the coder. This adds to

the reliability of the measure, which has been additionally been assured by other


42

techniques.

To obtain the score of KPI reporting in terms of quality for each company, an approach

to scoring has to be determined in terms of either weighted or un-weighted approaches.

According to the weighted disclosure approach, the researcher has to allocate weights to

the disclosure items based on each item’s importance. For example, Botosan (1997)

gave more weight to quantitative disclosures as she considered that quantitative

information is more important than qualitative information. Similarly, Boesso and

Kumar (2007) assigned more weight to forward-looking, quantitative, and non-financial

KPIs.

The main drawback of this approach is the subjective judgement involved in allocating

weights to the disclosed items in that it is likely that different coders may give different

assessments in terms of the items’ perceived importance.

The un-weighted approach avoids this key drawback of the weighted approach. Hence,

equal weights are attached to all disclosed items within the checklist. Therefore, if the

item is disclosed in the annual report, it is allocated "1" and “0” otherwise. Although

this approach is known as the ‘dichotomous’ method, Cooke (1991) demonstrates that it

is not strictly ‘dichotomous’ because some items may not be applicable to a firm. If this

is the case, these items are scored as ‘not applicable’ (NA).

The suggested ASB dimensions to capture the quality of KPI reporting are considered

to be integrated. Thus, there is no need to use the weighting approach with respect to

the proposed dimensions or the type of KPI information (i.e. financial or non-financial).

Hence, the qualitative characteristics of information are treated as equivalent with

regard to their importance. As discussed earlier when measuring KPI reporting quantity,

it is shown that there is no theoretical basis to weight either financial or non-financial

KPIs. Therefore, an un-weighted approach is also preferred to avoid subjectivity and


43

bias in measuring disclosure quality. Moreover, the current study is not focusing on one

particular user group. Finally, the current study agrees with the argument of Cooke

(1989) that weighting would be useless when research – such as the current research - is

not focusing on a particular user group. The approach adopted in the current study is

also supported by similar results reported for weighted and un-weighted scores in

previous studies (e.g. Firth, 1980).

On the other hand, the dichotomous scoring approach is applied to measure

KPI reporting quality. Therefore, ‘1’ was given if an item meets the quality

dimension and ‘0’ otherwise. The quality score for each KPI is calculated as a

ratio of the total items disclosed to ‘8’ (the maximum score for each KPI).

However, for non-financial KPIs, it is noted that it might not be applicable to

show any adjustments to any financial statement information used. Following

previous disclosure studies (e.g. Cooke, 1992; Tauringana and Mangena, 2009;

Tsalavoutas et al., 2010), this issue has been taken into consideration. Hence,

the quality score for a non-financial KPI is produced as a ratio of the total

items disclosed to ‘7’ instead of ‘8’ (the maximum number of applicable

disclosure items for each KPI). Then, the quality score for each company has

been derived as an average of its KPI quality score.


44

Table 2 An example of using the research instrument to assign quantity and quality to KPI reporting

Definitions of financial KPIs as provided in the annual report of Unilever PLC are: Sales growth: the percentage increase in turnover, adjusted for the impact of

acquisitions and disposals and exchange rate fluctuations. Underlying volume growth: underlying sales growth after eliminating the impact of price changes. Operating

margins: operating margin before the impact of restructuring costs, business disposals and other one-off items. Free cash flow: the cash flow from operating activities .

Return on invested capital (ROIC): The profit after tax (excluding finance and net impairment charges) divided by the average invested capital. Total shareholder return:

the returns received by a shareholder, capturing both the increase in share price and the value of dividend income (assuming dividends are re-invested).

Note: Non-financial KPIs are defined in the table.

Unilever PLC KPIs disclosed Quantity scoreThe definition The purpose Source of data Quantified targetCommentary Quantified datashowing adjustmentsDisclose changes Quality score for each KPI

Financial KPIs

2009 sales growth 1 1 1 1 0 0 1 1 0 0.625

Underlying volume growth 1 1 1 1 0 0 1 1 0 0.625

operating margin 1 1 0 0 0 0 1 0 0 0.250

Operating margin before 1 1 1 1 0 0 1 1 0 0.625

free cash flow 1 1 1 1 0 0 1 1 0 0.625

Return on invested capital 1 1 1 1 0 0 1 1 0 0.625

Total shareholder return 1 1 1 0 0 0 1 0 0 0.375

Financial KPIs reporting quantity 7 Financial KPIs reporting quality 0.536

Non financial KPIs

Total accident frequency rate 1 1 1 0 0 0 1 N/A 1 0.571

CO2 from energy per tonne of production (kg)1 1 1 0 1 1 1 N/A 0 0.714

Water per tonne of production (m3) 1 1 1 0 0 0 1 N/A 0 0.429

Total waste per tonne of production 1 1 1 0 0 0 1 N/A 0 0.429

Non-financial KPIs reporting quantity 4 Non-financial KPIs reporting quality 0.536

Total KPIs reporting quantity score 11 Total KPIs reporting quality score 0.536

CHAPTER TWO- KPI REPOTING IN UK: DESCRIPTIVE STUDY

45

2.3.1 Designing the research instrument & ensuring its validity

To design the initial research instrument, many considerations were taken into account.

First, the research instrument should be relevant to measuring both types of KPI

reporting in terms of quality and quantity for each company. Second, the eight KPI

reporting quality dimensions should be included within the initial checklist. Third, each

KPI coded is categorised into financial and non-financial in order to serve the purposes

of the analysis.

Validity is defined as the extent to which any instrument measures what it is intended to

measure (Marston and Shrives, 1991). Following previous disclosure studies (Hope,

2003; Tsalavoutas et al., 2010; Hassan and Marston, 2010), validity is ensured through

the assessment of content validity. Hence, it is achieved by relying on the literature

while constructing the instrument, so as to make sure that the instrument contains

relevant and adequate items with regard to measuring KPI disclosures.

Following previous studies (e.g. Tsalavoutas et al., 2010), after designing the initial

checklist, it was reviewed independently by both the principal and the second

supervisor in order to achieve instrument validity. All suggestions and comments were

discussed and considered in order to improve the validity of the instrument. Table 2

shows an example of using the research instrument to drive quantity and quality scores

of KPI reporting for Unilever Plc. in 2009.

2.3.2 Assessing the reliability of the research instrument: pilot study

Reliability is the extent to which the instrument produces the same results on repeated

trials (Hassan and Marston, 2010). Thus, the disclosure measure has to be subjected to

reliability tests in order to obtain useful inferences with regard to using the instrument

in a research situation (Beattie et al., 2004). Inter-rater reliability is the most frequently

reported measure when it comes to assessing reliability (Beattie et al., 2004). By


46

comparing the results produced by more than one coder, the greater the extent to which

the results are related, the more the reliable is the instrument. For instance, Linsley and

Shrives (2006) achieved reliability through coding an initial sample of seven annual

reports. The authors - in addition to the researcher who coded the whole sample of 79

annual reports - independently coded the initial sample. As the agreement level

exceeded 0.75, they considered this as a satisfactory level of inter-rater reliability.

To assess the reliability of the research instrument, a pilot study was conducted. It also

aimed to check the variation between firms in terms of KPI reporting using the research

instrument. The pilot study was conducted on a sample of ten annual reports for the

years 2009-2010. This sample was randomly selected from different sectors to measure

the quality and quantity of KPI reporting. Thus, the researcher was able to get an initial

idea about the variation between firms in different industries with respect to KPI

reporting.

Following the previous literature (e.g. Linsley and Shrives, 2006), decision rules were

produced and used as a coding reference to improve the reliability. Then, each of the

researcher and the two supervisors coded the annual reports of the pilot study sample

independently. This procedure aimed to ensure consistency in applying the decision

rules. Finally, the results obtained were checked, and found to be close.

Furthermore, parametric and non-parametric tests were performed to compare quality

and quantity scores given by the researchers who coded the same text9. Table 3

indicates that both the ANOVA and Kruskal-Wallis tests gave additional evidence of

the reliability of the research instrument10. The results in Table 3 show that there is no

9 At this point, it is difficult to decide whether the sample data came from a population with a Gaussian

(normal) distribution. In practice, the size of the sample was relatively small. 10

As having one independent variable with three groups (the researchers who coded the text) and one

dependent variable (quantity/quality scores), one way between groups analysis of variance, ANOVA was

employed (as a parametric test), and its equivalent non- parametric test (Kruskal-Wallis Test) was also

performed.


47

significant difference between the mean scores in terms of quality and quantity for the

main researcher and his supervisors.

Table 3 ANOVA and Kruskal-Wallis tests of differences among quantity and

quality scores in the pilot study

Researcher N Quantity

scores

mean

Quality

scores

mean

Quantity

scores

mean

rank

Quality

scores

mean

rank

The main researcher 10 12.9 0.498 15.45 14.50

1st supervisor 10 13.7 0.495 16.50 14.40

2nd supervisor 10 12.2 0.577 14.55 17.60

Total 30 12.93 0.524

ANOVA test:

F value

0.118

0.463

Kruskal-Wallis test:

Chi-Square

0.247

0.857

Significance levels 0.889 0.634

0.884 0.651

Finally, the discrepancies between the coders’ scores were analysed. Any issues that led

to differences were resolved. Actually, there were few differences which were mainly

related to KPI classification issues. For instance, a disagreement came from particular

KPIs such as order book\ orders received \revenue; or sales per employee\ average

room rate\ licenses signed\ growth in passenger journeys\miles\ unique active

players\market share. Thus a rule is set in order to consider such a KPI as a financial

KPI, because they are related and\or can be derived directly from financial statements.

Ensuring that the disclosure measure is reliable and valid is an essential procedure

before applying the research instrument to the main study sample.

2.4 Data

The current study focuses on analysing KPI reporting for a sample of FTSE 350 non-

financial UK firms. Panel (A) in Table 4 shows the sample selection process that starts

from focusing on the top 350 UK firms. Hence, the Financial Times ranking for 2011 is


48

used to define these companies based upon their market capitalisation value. Then,

financial firms are excluded in order to identify the sample, following previous studies

(e.g. Beretta and Bozzolan (2004); Abraham and Cox (2007)), because these firms have

specific characteristics as well as a different framework for disclosure practices

applicable to them. Following Elshandidy et al. (2013), firms with missing financial or

corporate governance data are removed. Considering the time and effort needed in

coding each annual report, this procedure is used in order to retain firms with a

complete time series of data. Hence, the number of observations would not significantly

drop in the next stage of the analysis because of the problem of missing data. Thus, the

rule used to remove firms at this stage is: a firm should be excluded when it has one

type of financial data which is missing for more than one year; or if more than one type

of this data is missing for one year. Moving from the resultant firms (190 firms); sample

firms are randomly selected from all possible sectors. Every sector is represented in the

sample according to the following equation:

Where;

: represents the number of firms that have to be chosen from the sector .

: represents the total number of firms included sector .

: represents the total number of firms identified for all sectors (i.e., 190 firms).

Moving on from this, systematic sampling is used to select sample firms from each

sector. Given that firms are initially ordered according to market capitalisation, the first

firm in each sector is considered as the starting point, and then the process is continued

by selecting the third, the fifth and so on. Following this procedure, 103 firms are

identified (515 firm-year observations over the five year period 2006-2010).

Subsequently, various observations are excluded for the reasons illustrated in Panel (A)


49

in Table 4, to end up with 503 observations as the final sample. Panel (B) in the same

table provides a disaggregation of the sample across industries.

Firms’ annual reports are collected from the company homepages and from the

Thomson One Banker database. Data on firms’ financial characteristics are collected

from Datastream. The developed research instrument is employed to quantify KPI

reporting, and to assign quality scores based upon ASB guidance for best practice.

Table 5 illustrates the definition and measurement for each variable of the present

study.

Table 4 Sample Selection and its disaggregation across industries

PANEL A – SAMPLE SELECTION PROCESS

Starting point: Top 350 UK firms based on market capitalisation, according to the 2011

Financial Times ranking. Financial firms are then excluded. Subsequently, 103 firms are selected randomly following two criteria: 1) each sector is represented in the same

proportion as in the starting sample; 2) as firms are arranged according to market capitalisation; systematic sampling is used by choosing the first company in every sector as a starting point. Then, selection is continued by selecting the third, the fifth

and so on. This process results in 515 observations [103 * 5 years (2006, 2007, 2008, 2009, and 2010)]. Thereafter, the following exclusions take place:

n observations

excluded

thereafter

Reason for exclusion

2 KPI regulation not applicable in 2006 11

4 Missing data on directors’ compensation 6 Missing CG data

12 total number of observations excluded

503 final sample

PANEL B – SAMPLE CONSTITUENTS BY INDUSTRY

Industry Frequency Percentage

Basic Materials 40 8.0

Consumer Goods 65 12.9 Consumer Services 107 21.3

Health Care 24 4.7 Industrials 143 28.4

Oil & Gas 54 10.7

11

Two observations have been excluded as these companies’ financial year started before 1-4-2005 (the

date at which the requirement to include a business review in annual reports in the UK became

applicable).

http://ezproxy.stir.ac.uk/login?url=http://banker.thomsonib.com/ta/?ExpressCode=Stirling


50

Technology 40 8.0 Telecommunications 10 2.0

Utilities 20 4.0

TOTAL 503 100

Table 5 Study variables: definitions & measurement

Panel (A) KPI reporting quantity

Variable Definition Measurement

QNFKS Quantity of

financial KPI reporting

The number of financial KPIs

disclosed in KPI section.

QNNFKSEC Quantity of non-

financial KPI reporting

The number of non-financial KPIs

disclosed in the KPI section.

QNNFKREP Quantity of total non- financial KPI

reporting

The number of non-financial KPIs disclosed in the whole report.

QNTKSEC Quantity of KPI reporting

The total number of financial and non-financial KPIs disclosed in the

KPI section.

QNTKREP Quantity of total KPI reporting

The total number of financial and non-financial KPIs disclosed in the

whole report.

Panel (B) KPI reporting quality


QLFKS Quality of financial KPIs reported

The aggregated quality score of financial KPIs that are disclosed in the KPI section.

QLNFKSEC Quality of non-financial KPIs reported

The aggregated quality score of non-financial KPIs disclosed in the KPI section

QLNFKREP Quality of total non-financial KPIs reported

The aggregated quality score of non-financial KPIs that are disclosed in the whole report

QLTKSEC Quality of KPIs reported

The aggregated quality score of financial and non-financial KPIs that are disclosed in the KPI section.

QLTKREP Quality of total KPIs reported

The aggregated quality score of financial and non-financial KPIs that are disclosed in the whole report

Panel (C) Firm characteristics


SIZE Firm size The natural logarithm of market capitalisation (WC08001)

PROFITAB Profitability The profitability measured by return on equity ((WC01651) / (WC03501))

LEVERAGE Leverage The ratio of total debt to total capital (WC08221)

LIQUIDITY Liquidity The current assets (WC02201) / current liabilities (WC03101)

DIVYIELD Dividend yield Dividends per share / share price


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((WC05376)/(WC08001))

CROSSLIST Cross listing Dummy variable equals to 1 if the firm’s shares are traded on foreign financial markets and 0 otherwise.

Table 6 gives a full picture of the characteristics of the sample firms. Panel (A) reports

the descriptive analysis for the continuous variables. It shows that the natural logarithm

of market capitalisation for these firms varies from a minimum of 8.00 (£16,506,000) to

a maximum of 11.019 (£130.16 billion) with standard deviation of 0.685 (£17.8

billion). This significant variation is expected, as the sample firms are drawn from

FTSE 350 which includes the largest UK firms. It shows that firm size should be

considered as it might have effects on KPI reporting in practice. However, the large

variation may refer also to the existence of outliers which will be identified and

addressed later in chapter three. These firms’ profitability mean measured by ROE is

0.08 which refers, in general, to firms’ ability to generate profits from shareholders’

equity. However, the value of the ROE ratio should exceed the cost of equity capital in

order to add value to shareholders. The liquidity ratio median is 1.64 times which

indicates that firms in the sample do not suffer from financial problems in short run. It

shows that firms are able to cover their short term liabilities through their current assets.

These companies are not highly leveraged, with a mean debt to total capital of 0.364.

However, the minimum of zero and maximum of 1.42 for the leverage ratio indicates

that these firms are varied to some extent in their reliance on debt to finance their

investments.12 Finally, the sample firms have a percentage of dividends to share price

with median of 2.4%. As companies display a good ability to secure current liabilities,

it may be implied that these firms may prefer to retain profits in order to finance their

growth.

12

Checking the annual reports showed that some companies have a leverage ratio of zero (e.g. Premier

Oil, 2006). It seems that these companies do not rely on debt at all, which can be explained by their

strong cash position. On the other hand, other companies display a very high leverage ratio (e.g. Severn

Trent, 2009) due to the huge losses they made.


52

Panel (B) points out the analysis of cross listing as a categorical variable. It indicates

that the majority of sample firms (90.29 %) are traded on foreign financial markets.

Table 6 Descriptive statistics of continuous variables

Panel (A) Descriptive statistics for the continuous variables

Variable Max Min Mean Med SD N

SIZE 11.019 8.000 9.195 9.067 0.685 503

PROFITAB 0.524 -0.172 0.080 0.067 0.087 503

LIQUIDITY 8.574 0.268 1.644 1.329 1.320 503

LEVERAGE 1.420 0.000 0.364 0.337 0.279 503

DIVYIELD 0.219 0.000 0.029 0.024 0.032 503

Panel (B) Descriptive statistics for the categorical variable

Variable Proportion N

CROSSLIST 90.29 503 Panel A displays descriptive statistics of continuous variables used in the present study as proxies for

firm characteristics: SIZE is the natural logarithm of market capitalization; PROFITAB is the

profitability measured by return on equity (the ratio of net income to book value of equity); LIQUDITY

is measured by the current ratio; LEVERAGE is calculated as the ratio of total debt to total capital;

DIVYIELD is a proxy for dividend policy (dividends per share / share price). Panel (B) displays the descriptive statistics for the categorical variable: CROSSLIST is a dummy

variable equals to1 if the firm’s shares are traded on foreign financial markets and 0 otherwise.

After getting this idea about the characteristics of the sample firms, their KPI reporting

is analysed. Accordingly, the main features of KPI reporting in UK firms could be

observed, and hence, Q1 will be addressed.

2.5 Findings of the analysis

This section aims to provide answers to research question 1. Descriptive statistics

illustrate the main features of KPI reporting in terms of quantity and quality on the part

of UK firms. It offers insights into KPI reporting in terms of quantity and quality in the

sample firms. In general, descriptive results are used to show KPI reporting in practice

through giving examples from the firms’ annual reports, analysing KPI disclosures and

its subcategories to study the development over the period 2006-2010, and exploring

any changes in KPI reporting in terms of quantity and quality across industries.


53

2.5.1 Companies’ disclosures

This section aims to answer the first research question. It provides some direct quotes

from companies’ annual reports to illustrate the attitude of these companies regarding

KPI reporting.

In general, there are many examples of practice which indicate that KPI reporting is

most likely to be voluntary. For instance, 49 companies did not provide any information

regarding their KPIs in their annual reports. One of the commentaries on this practice

was:

‘The group is a pure exploration group with no production or proven reserves, the

standard KPIs are not relevant. The management therefore focuses on the

achievement of work programmes and protection of licences. Throughout the year,

the management has exceeded minimum work programme requirements, and

licences have therefore been protected’ (Rockhopper Exploration, 2009, p. 13).

Despite that, most companies were keen to provide financial KPIs. This type of KPI had

not been reported in 56 year observations. On the other hand, it is apparent that

companies did not show the same concern with regard to disclosing non-financial KPIs.

It is found that the disclosure of non-financial KPIs was absent in 196 year-

observations. From these observations, 23 companies did not provide any non-financial

KPI-related information for the period examined (2006-2010). One of these companies

gave the following justification:

‘In addition to financial KPIs, the board considers non-financial factors such as

the group compliance with corporate governance standards and environmental

considerations relevant to some of the group’s mining interests. These factors

cannot be efficiently measured, so do not form part of the group’s KPIs’ (Anglo


54

Pacific Group, 2008, p. 10).

Additionally, the majority of companies did not separate KPIs in terms of financial and

non-financial KPIs. Furthermore, there is no general rule to classify what can be

considered as financial or non-financial KPI. Thus, many classification differences

existed in practice, as each company relied on its own rule to categorise the disclosed

KPIs. For example, Research and Development is considered as a financial KPI by the

majority of companies. However, Cookson Group reported it as a non-financial KPI

(Cookson Group, 2008).

In contrast, a number of companies showed good practice regarding KPI reporting, For

example, when one of its KPIs was replaced; Hikma Pharmaceuticals Plc. stated:

‘We are no longer including R&D costs as a percentage of revenue as a KPI as

this is no longer the best way to measure our investment in our pipeline, given the

increase in spending on product acquisitions. This year, however, we have added

new product launches as a non-financial KPI’ (Hikma Pharmaceuticals Plc.,

Annual Reports and Accounts 2007, p. 16).

Similarly, Pace Plc. presented a new KPI by indicating the reason behind abandoning

the previous KPI:

‘Going forwards we are introducing return on sales (ROS) as a new performance

indicator after concentrating on gross margin for the last few years. Now as our

product mix changes, as we target high and low-end opportunities and start to

rollout infrastructure products from our Networks group, margin is no longer the

best overall measure of success. Refocusing our business around ROS is helping

to establish a new mind-set, as we take Pace to the next level’ (Pace Plc, Annual

Reports and Accounts 2008, p. 5).

What is worth noting is the fact that all companies with the exception of small ones, are


55

asked to publish financial and non-financial KPIs. However, the above examples give

another view on the nature of KPI reporting. It reveals that companies are controlling

KPI disclosures in practice. That leads to an expectation, which is that KPI reporting

scores might vary across the sample firms. That variation can be observed by analysing

the quality and quantity scores of KPI reporting. To start the analysis, it is noted that the

majority of firms allocate specific section for KPIs or refer to pages that contain KPI

information, while other firms do not. To highlight this practice, the study distinguishes

between KPI disclosures in the KPI section (which also includes KPI disclosures within

the section(s)/ page(s) that are mentioned by the firm in the report), and KPI disclosures

in the whole report (which include the KPIs disclosed in the KPI section as well as KPIs

disclosed elsewhere). Accordingly, the aggregated scores - either for quantity of KPI

reporting or for its quality - are disaggregated based upon these two categories

(financial and non-financial KPIs). The next subsection starts with analysing KPI

reporting with regard to its quantity.

2.5.2 Descriptive statistics for the quantity of KPI reporting

Table 7 provides a descriptive analysis for KPI quantity scores. It indicates that the

number of KPIs disclosed in the KPI section (QNTKSEC) by the sample firms ranges

from a minimum of zero to a maximum of 24 KPIs. The median of QNTKSEC is 7

KPIs. It seems that most of the KPIs disclosed are financial KPIs. The median of

financial KPIs disclosed in the KPI section (QNFKS) is 5 KPIs, while the median of the

non-financial KPIs disclosed in the KPI section (QNNFKSEC) is only one KPI. After

considering the KPIs disclosed outside the KPI section, the median of non-financial

KPIs disclosed in the whole report (QNNFKREP) is found to be 2 KPIs. It appears that

KPIs disclosed outside the KPI section are more likely to be one non-financial KPI.

This conclusion is confirmed when comparing the mean of QNTKSEC - 7.48 KPIs -


56

with the mean of the QNTKREP 8.18 KPIs. Generally, the high standard deviation

values show the high variation in KPI reporting quantity among the sample firms.

Table 7 Descriptive statistics for KPI quantity scores


QNTKSEC 24.00 0.00 7.48 7.00 5.03 503

QNFKS 19.00 0.00 5.34 5.00 3.44 503

QNNFKSEC 15.00 0.00 2.17 1.00 2.91 503

QNNFKREP 16.00 0.00 2.87 2.00 3.40 503

QNTKREP 24.00 0.00 8.18 7.00 5.36 503

QNTKSEC is the total number of financial and non-financial KPIs disclosed in KPI’ section; QNFKS is

financial KPIs disclosed in KPI’ section; QNNFKSEC is non-financial KPIs disclosed in KPI’ section;

QNNFKREP is non-financial KPIs disclosed in the whole report; QNTKREP is the total number of

financial and non-financial KPIs that disclosed in the whole report.

2.5.2.1 The frequency of the disclosed KPIs

Table 8 shows the KPIs disclosed most frequently by the sample firms. Panel (A)

illustrates that the highest financial KPI disclosed is revenues. This is reported in 32%

of the year-observations, followed by underlying earnings per share (25%), free cash

flow (22%), basic earnings per share (22%), and return on capital employed (ROCE)

(21%).

Panel (B) show that the most frequently reported non-financial KPI is accident incident

rate (AIR) – which is disclosed 146 times over 503 observations. Other non-financial

KPIs that are widely disclosed are employee turnover\retention (13%), accident

numbers (11%), energy consumption (10%), and carbon dioxide emissions (10%).

Table 8 The frequency of KPIs disclosed by the sample firms

Panel (A) The frequently reported financial KPIs

KPI Frequency Percentage

Revenues 161 32%

Underlying earnings per share 126 25%

Free cash flow 111 22%

Basic earnings per share 110 22%

Return On Capital Employed (ROCE) 106 21%

Total sales growth 91 18%

Operating profit margin 86 17%


57

Organic revenue growth 75 15%

Panel (B) The frequently reported non-financial KPIs

KPI Frequency Percentage

Accident Incident Rate (AIR) 146 29%

Employee turnover\ retention 65 13%

Accident numbers 55 11%

Energy consumption 51 10%

Carbon Dioxide emitted 50 10%

Waste to landfill 50 10%

Water consumption 40 8%

Average Headcount 25 5%

2.5.2.2 Quantity of KPI reporting across the sample period

The trend in the quantity of KPI reporting is analysed by following the descriptive

statistics from 2006 to 2010. In Table 9, it is observed that the quantity of KPI

reporting, as well as its subcategories, have been increasing across the sample period

(2006-2010).

It is documented that the mean (median) of QNTKSEC was 5.52 (5.0) KPIs in 2006. It

then shows a steady increase across the sample period to reach a mean (median) of 8.6

(7.5) KPIs in 2010. Similarly, the mean (median) of QNFKS has increased from 4.15

(4.0) KPIs in 2006 to reach a mean (median) of 5.98 (6) KPIs in 2010. The results

illustrate that the number of financial KPIs disclosed is always greater than non-

financial ones. However, the mean (median) of QNNFKSEC has increased from 1.42

(0) KPIs in 2006 to reach a mean (median) of 2.61 (2) KPIs in 2010. Thus, it is shown

that firms pay particular attention to disclosing more KPIs in general and non-financial

ones in particular. This can be observed also when considering non-financial KPIs

disclosed outside the KPI section, which have increased during the same period. The

mean (median) of QNNFKREP has increased from 1.76 (1) KPIs in 2006 to reach a

mean (median) of 3.90 (3) KPIs in 2010. As a result, KPIs reported in the whole report


58

have increased from a mean (median) of 5.83 (5.0) KPIs in 2006 to a mean (median) of

9.89 (9) KPIs in 2010.

Table 9 Quantity of KPI reporting across years

2006


QNTKSEC 24.00 0.00 5.52 5.00 4.79 96

QNFKS 15.00 0.00 4.15 4.00 3.38 96

QNNFKSEC 13.00 0.00 1.42 0.00 2.50 96

QNNFKREP 16.00 0.00 1.76 1.00 2.88 96

QNTKREP 24.00 0.00 5.83 5.00 4.90 96

2007


QNTKSEC 24.00 0.00 7.03 6.00 5.18 101

QNFKS 19.00 0.00 5.18 5.00 3.58 101

QNNFKSEC 15.00 0.00 1.93 1.00 3.01 101

QNNFKREP 16.00 0.00 2.30 1.00 3.20 101

QNTKREP 24.00 0.00 7.40 6.00 5.29 101

2008


QNTKSEC 24.00 0.00 7.88 7.00 5.15 102

QNFKS 19.00 0.00 5.59 5.00 3.38 102

QNNFKSEC 15.00 0.00 2.31 1.00 3.10 102

QNNFKREP 16.00 0.00 2.89 2.00 3.39 102

QNTKREP 24.00 0.00 8.45 7.00 5.42 102

2009


QNTKSEC 24.00 0.00 8.26 7.00 4.82 102

QNFKS 19.00 0.00 5.75 5.50 3.25 102

QNNFKSEC 15.00 0.00 2.51 2.00 2.93 102

QNNFKREP 15.00 0.00 3.41 3.00 3.41 102

QNTKREP 24.00 0.00 9.17 8.00 5.12 102

2010


QNTKSEC 24.00 0.00 8.60 7.50 4.68 102

QNFKS 19.00 0.00 5.98 6.00 3.37 102

QNNFKSEC 15.00 0.00 2.61 2.00 2.84 102

QNNFKREP 16.00 0.00 3.90 3.00 3.66 102

QNTKREP 24.00 0.00 9.89 9.00 5.21 102

QNTKSEC is the total number of financial and non-financial KPIs disclosed in the KPI section;

QNFKS is financial KPIs disclosed in the KPI section; QNNFKSEC is non-financial KPIs disclosed in

the KPI section; QNNFKREP is non-financial KPIs disclosed in the whole report; QNTKREP is the

total number of financial and non-financial KPIs disclosed in the whole report.


59

As mentioned above, it seems that there is a variation in the quantity of KPIs reported

during the sample period.13 However, the study compares KPI reporting quantity across

the sample period to test whether or not the differences between these scores are

significant. Table 10 indicates that an F value of 8.62 is significant, suggesting that the

means are not all equal. Furthermore, a Bonferroni test is carried out to identify where

the differences in the quantity scores are. The test makes multiple comparisons in order

to examine the differences between each pair of quantity score means. The results

reported in Table 10 illustrate that there is an increasing trend in the quantity of KPIs

reported between 2006 and 2010. However, there are significant differences between

the quantity scores’ mean of 2006 and the quantity scores’ means of 2008, 2009, and

2010 at a significance level of 5%. Moreover, the results show a statistically significant

difference between the quantity scores’ mean of 2007 and the quantity scores’ mean of

2010, but at the 10% level. Thus, Table 44 in Appendix (1) indicates that the latter

difference becomes significant at a level of 5%, if the non-financial KPIs disclosed -

outside the KPI section - are considered. With respect to the quantity of financial and

non-financial KPI reporting, Table 41 and Table 42 in Appendix (1) confirm that

significant differences exist only between the quantity scores’ mean of 2006 and the

quantity scores’ means of 2008, 2009, and 2010 at a level of 5%. These results suggest

that, despite the significant differences between quantity scores in general, those

differences are mainly in correspondence with the quantity scores of 2006.

Interestingly, these tests do not provide evidence suggesting that the financial crisis in

2008 influenced KPI reporting in terms of quantity. For instance, it appears that there

are no significant differences between the quantity scores’ means of 2009 and 2010, and

those of 2007. This might indicate that companies did not use KPI reporting as a tool to

13

That trend is also tested using Cuzick test - developed by Cuzick (1985) - to test for trends across

ordered groups. The results of that non-parametric test documents a statistically significant trend in all

proxies of KPI reporting in terms of quantity at a level of 1% (results are not tabulated).


60

communicate with annual report users with respect to the consequences of the financial

crisis.

Table 10 Anova test to compare KPI reporting quantity across the sample period

QNTKSEC is the total number of financial and non-financial KPIs disclosed in the KPI section.

2.5.2.3 Quantity of KPI reporting across industries

The quantity of KPI reporting disclosed across industries is analysed, in order to shed

light on any possible variations between industries in practice. Table 11 shows that

industries are - to some extent - varied in terms of KPI reporting quantity. The highest

number of KPIs is provided by Utilities firms, either when considering KPIs disclosed

outside the KPI section or not. The mean number of QNTKSEC in Utilities firms is

15.25 KPI, while the mean of QNTKREP is 15.8 KPIs. Accordingly, the highest

0.000 0.057 1.000 1.000 2010 .799779 .423377 .186328 .085113 0.000 0.270 1.000 2009 .714667 .338264 .101216 0.001 1.000 2008 .613451 .237049 0.154 2007 .376402 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QNTKSEC by year

Bartlett's test for equal variances: chi2(4) = 14.4918 Prob>chi2 = 0.006

Total 628.344774 502 1.25168282 Within groups 587.647379 498 1.18001482Between groups 40.6973947 4 10.1743487 8.62 0.0000 Source SS df MS F Prob > F Analysis of Variance

Total 2.4966587 1.1187863 503 2010 2.7893563 .9083085 102 2009 2.7042436 .98009983 102 2008 2.603028 1.0568603 102 2007 2.3659792 1.2023142 101 2006 1.9895769 1.2562748 96 year Mean Std. Dev. Freq. Summary of QNTKSEC


61

number of financial and non-financial KPIs is disclosed by Utilities firms. The mean of

QNFKS is 6.95 KPIs; the mean of QNNFKSEC is 8.9 KPIs, while after considering

KPIs disclosed outside the KPI section, the mean of QNNFKREP is 9.6 KPIs. It is

worth mentioning that, Utilities is the only industry which disclosed more non-financial

KPIs than financial ones.

In contrast, Table 11 indicates that the lowest number of KPIs disclosed is shown in the

case of Healthcare firms. The mean number of QNTKSEC in these firms is 3.67 KPIs,

while it becomes 5.67 (the mean of QNTKREP) if KPIs disclosed outside the KPI

section are considered. Moreover, Healthcare firms show the lowest numbers of

financial and non-financial KPIs disclosed among the sample industries. The median of

QNFKS is 3 KPIs, whereas the median of QNNFKSEC is zero, and the median of

QNNFKREP is 2 KPIs.

Table 11 Quantity of KPI reporting across industries

Basic Materials


QNTKSEC 24.00 0.00 7.75 6.00 5.31 40

QNFKS 11.00 0.00 4.53 5.00 2.59 40

QNNFKSEC 15.00 0.00 3.28 2.00 4.01 40

QNNFKREP 16.00 0.00 3.55 2.00 4.13 40

QNTKREP 24.00 0.00 8.00 6.00 5.46 40

Consumer Goods


QNTKSEC 14.00 0.00 7.31 7.00 3.55 65

QNFKS 12.00 0.00 5.94 7.00 2.78 65

QNNFKSEC 5.00 0.00 1.37 1.00 1.77 65

QNNFKREP 8.00 0.00 2.03 1.00 2.54 65

QNTKREP 20.00 0.00 7.97 7.00 4.32 65

Consumer Services


QNTKSEC 24.00 0.00 7.99 7.00 5.60 107

QNFKS 19.00 0.00 5.88 6.00 4.31 107

QNNFKSEC 10.00 0.00 2.09 1.00 2.50 107

QNNFKREP 12.00 0.00 2.57 2.00 2.84 107

QNTKREP 24.00 0.00 8.47 7.00 5.81 107


62

Healthcare


QNTKSEC 6.00 0.00 3.67 4.00 1.88 24

QNFKS 6.00 0.00 2.83 3.00 1.63 24

QNNFKSEC 4.00 0.00 0.83 0.00 1.27 24

QNNFKREP 10.00 0.00 2.83 2.00 3.36 24

QNTKREP 15.00 0.00 5.67 4.50 4.29 24

Industrials


QNTKSEC 19.00 0.00 7.39 6.00 4.48 143

QNFKS 13.00 0.00 5.37 5.00 3.31 143

QNNFKSEC 10.00 0.00 2.02 2.00 2.23 143

QNNFKREP 16.00 0.00 2.77 2.00 2.81 143

QNTKREP 20.00 0.00 8.14 7.00 4.50 143

Oil & Gas


QNTKSEC 18.00 0.00 6.52 6.00 5.15 54

QNFKS 9.00 0.00 4.39 4.50 3.22 54

QNNFKSEC 10.00 0.00 2.13 2.00 2.43 54

QNNFKREP 10.00 0.00 2.30 2.00 2.65 54

QNTKREP 18.00 0.00 6.69 6.00 5.31 54

Technology


QNTKSEC 14.00 0.00 6.13 5.50 3.05 40

QNFKS 11.00 0.00 5.48 5.50 2.83 40

QNNFKSEC 5.00 0.00 0.65 0.00 1.25 40

QNNFKREP 9.00 0.00 1.68 0.00 2.49 40

QNTKREP 18.00 0.00 7.15 6.00 4.50 40

Telecommunications


QNTKSEC 15.00 0.00 7.70 7.50 4.88 10

QNFKS 12.00 0.00 6.00 5.50 3.46 10

QNNFKSEC 4.00 0.00 1.70 2.00 1.64 10

QNNFKREP 14.00 0.00 4.60 2.00 5.97 10

QNTKREP 23.00 0.00 10.60 8.50 8.49 10

Utilities


QNTKSEC 24.00 0.00 15.25 17.00 6.58 20

QNFKS 17.00 0.00 6.95 5.50 3.93 20

QNNFKSEC 15.00 0.00 8.90 9.00 5.01 20

QNNFKREP 16.00 2.00 9.60 10.00 4.91 20

QNTKREP 24.00 4.00 15.80 17.00 6.12 20

QNTKSEC is the total number of financial and non-financial KPIs disclosed in the KPI section; QNFKS

is financial KPIs disclosed in the KPI section; QNNFKSEC is non-financial KPIs disclosed in the KPI

section; QNNFKREP is non-financial KPIs disclosed in the whole report; QNTKREP is the total number


63

of financial and non-financial KPIs that are disclosed in the whole report.

Furthermore, the study compares KPI reporting quantity scores across industries to test

whether the differences between industries in quantity scores are significant.

Table 52 in Appendix (1) shows that the means are not all equal as F value is

significant. A Bonferroni test conducts multiple comparisons between each pair of

quantity scores. Generally, the results reported in

Table 52 show that Utilities firms report a number of KPIs which is statistically

significant and higher than the rest of the industries. Rather, in spite of disclosing a

higher number of KPIs than other industries,

Table 52 reports that the differences between the quantity scores of Basic Material

firms and those of other industries are not significant. In contrast,

Table 52 shows that Healthcare firms present a number of KPIs which is lower than that

of other industries. Hence, the differences between quantity scores of Healthcare firms

and those of Consumer Goods, Consumer Services, Industrials, and Utilities firms are

statistically significant. Yet, as indicated in Table 53 in Appendix (1), these differences

turned to be insignificant (except those with regard to Utilities firms) when non-

financial KPIs disclosed - outside the KPI section - are considered. Concerning, the

quantity of financial KPI reporting, Table 49 in Appendix (1) illustrates that most of the

differences between quantity scores among the sample industries are not significant. On

the other hand, Table 50 indicates that Utilities firms report a higher number of non-

financial KPIs than other industries. In contrast, Technology firms provides a

statistically significant and lower number of non-financial KPIs than firms in Basic

Materials, Consumer Services, Industrials, Oil & Gas, and Utilities industries.


64

2.5.3 Descriptive statistics for quality of KPI reporting

Table 12 shows the general statistics with regard to KPI reporting quality scores. It

seems that companies are widely varied in terms of KPI disclosure quality. For

instance, the quality level for KPIs -disclosed in the KPI section- range from 0 to 0.688.

Yet, it is obvious that all quality means and medians record a remarkably low level.

For those KPIs disclosed in the KPI section, the mean (median) of financial KPI

reporting quality (QLFKS) is 0.347 (0.375). The level of non-financial KPI reporting

quality (QLNFKSEC) is lower with a mean (median) of 0.268 (0.286). As a result, the

mean (median) of KPI reporting quality (QLTKSEC) is 0.363 (0.375). However, it

seems that a high quality of KPI reporting outside the KPI section has driven the total

quality of KPI reporting QLTKREP to be slightly higher than the corresponding

QLTKSEC, with a mean (median) of 0.371 (0.390).

Table 12 Descriptive statistics for KPI quality scores


QLTKSEC 0.688 0.00 0.363 0.375 0.174 503

QLFKS 0.691 0.00 0.347 0.375 0.176 503

QLNFKSEC 0.786 0.00 0.268 0.286 0.250 503

QLNFKREP 0.818 0.00 0.309 0.333 0.252 503

QLTKREP 0.665 0.00 0.371 0.390 0.170 503

QLTKSEC is the aggregated quality of financial and non-financial KPIs disclosed in the KPI section;

QLFKS is the quality of financial KPIs disclosed in the KPI section; QLNFKSEC is the quality of non-

financial KPIs disclosed in the KPI section; QLNFKREP is the quality of non-financial KPIs disclosed

in the whole report; QLTKREP is the aggregated quality of financial and non-financial KPIs disclosed

in the whole report.

As quality scores are identified as being based upon the ASB guidelines, it would be

useful to explore companies’ practices with respect to the eight dimensions used to

evaluate KPI quality.


65

2.5.3.1 KPI reporting quality: qualitative attributes in practice

Table 13 displays the individual averages with regard to the 8 qualitative attributes

recommended by the ASB (2006). It is indicated that the majority of firms display their

information by considering two recommendations. It is shown that firms provide the

definition for 86.9% of the KPIs disclosed in the KPI section and 87.4% of the KPIs

disclosed in the whole report. Additionally, firms quantify KPI data for 79.6 % of the

KPIs disclosed in the KPI section and 79.7% of the KPIs disclosed in the whole report.

On the other hand, companies show a modest tendency to explain the purpose of

presenting each KPI. The purpose is explained in 48.8% of KPIs disclosed in the KPI

section, and 51.1% of the KPIs disclosed in the whole report. In contrast, firms show a

very weak reporting practice with regard to other attributes of KPI reporting quality.

In line with the arguments discussed earlier in this chapter, it worth noting that the

qualitative attributes is integrated to produce useful KPI information for the reader.

Thus, it can be claimed that annual report users might find this information irrelevant in

terms of their decision making.14 On the other hand, these results confirm and explain

the above conclusion about the low level of KPI reporting in general. The results also

suggest that reporting quality is higher as long as KPIs disclosed outside the KPI

section are included in the analysis. However, these findings raise questions about the

need to introduce clear guidelines and benchmarks concerning KPI disclosure, and the

mechanisms required in order to make firms more compliant.

Table 13 KPI reporting quality: descriptive statistics of individual dimensions

Dimensions suggested by OFR (2006) KPI section The whole report

Provision of the definition 0.869 0.874

Explanation of the purpose 0.488 0.511

14

This could limit the conclusions made about the implications of KPI reporting quality in particular.


66

Disclosure of the source 0.294 0.301

Quantify the targets 0.098 0.104

Commentary on future targets 0.053 0.059

Quantify the data 0.796 0.797

Disclosing F.S data adjusted 0.183 0.182

Disclosure of any changes 0.041 0.044

The following subsections shed more light on the quality of KPI reporting by analysing

quality scores throughout the sample period, as well as studying it across industries.

2.5.3.2 Quality of KPI reporting across the sample period

Table 14 illustrates the statistics with regard to KPI reporting quality for the full sample

across the period under consideration. It is shown that KPI reporting quality has

increasing during the period 2006-2010. The mean (median) of QLTKSEC starts with

0.285 (0.295) in 2006, then increases across the sample period to reach a mean

(median) of 0.414 (0.423) in 2010. It seems that the quality of non-financial KPI

reporting outside the KPI section is usually higher than the corresponding figure with

regard to QLNFKSEC. As a result, QLNFKREP leads QLTKREP in term of being

always higher than the level of QLTKSEC throughout the sample period. QLTKREP

has increased from a mean (median) of 0.288 (0.301) in 2006 to reach its maximum in

2010 with a mean (median) of 0.426 (0.436).

Looking at the trend with regard to financial and non-financial KPI reporting, Table 14

shows that the quality levels show a steady increase over the sample period. Yet, the

figures indicate that QLFKS is always higher than QLNFKSEC as well as QLNFKREP.

For instance, the mean (median) of QLFKS in 2006 was 0.264 (0.282), whereas the

mean (median) of QLNFKSEC and QLNFKREP in the same year were 0.191 (0.000)

and 0.215 (0.136) respectively. The improvement in quality levels is shown in the

corresponding statistics for the next years covered. For example, the mean (median) of


67

QLFKS in 2010 becomes 0.402 (0.404), whereas the mean (median) of QLNFKSEC

and QLNFKREP in the same year reaches a level of 0.320 (0.346) and 0.380 (0.429)

respectively. These results suggest that the low level of non-financial KPI reporting in

terms of quality causes the overall level of KPI reporting in terms of quality to be

lower.

Table 14 Quality of KPI reporting across the years

2006


QLTKSEC 0.688 0.00 0.285 0.295 0.194 96

QLFKS 0.691 0.00 0.264 0.282 0.192 96

QLNFKSEC 0.786 0.00 0.191 0.000 0.237 96

QLNFKREP 0.818 0.00 0.215 0.136 0.241 96

QLTKREP 0.665 0.00 0.288 0.301 0.190 96

2007


QLTKSEC 0.688 0.00 0.342 0.357 0.180 101

QLFKS 0.691 0.00 0.326 0.357 0.176 101

QLNFKSEC 0.786 0.00 0.252 0.286 0.249 101

QLNFKREP 0.818 0.00 0.284 0.286 0.250 101

QLTKREP 0.625 0.00 0.348 0.360 0.176 101

2008


QLTKSEC 0.688 0.00 0.379 0.391 0.163 102

QLFKS 0.691 0.00 0.362 0.375 0.165 102

QLNFKSEC 0.786 0.00 0.279 0.286 0.251 102

QLNFKREP 0.786 0.00 0.318 0.357 0.251 102

QLTKREP 0.665 0.00 0.387 0.399 0.157 102

2009


QLTKSEC 0.680 0.00 0.392 0.394 0.157 102

QLFKS 0.691 0.00 0.374 0.385 0.166 102

QLNFKSEC 0.786 0.00 0.292 0.295 0.248 102

QLNFKREP 0.818 0.00 0.345 0.429 0.249 102

QLTKREP 0.665 0.00 0.400 0.418 0.154 102

2010


QLTKSEC 0.688 0.00 0.414 0.423 0.144 102

QLFKS 0.691 0.00 0.402 0.404 0.148 102

QLNFKSEC 0.786 0.00 0.320 0.346 0.249 102

QLNFKREP 0.818 0.00 0.380 0.429 0.243 102


68

QLTKREP 0.666 0.00 0.426 0.436 0.141 102

QLTKSEC the aggregated quality of financial and non-financial KPIs disclosed in the KPI section;

QLFKS is the quality of financial KPIs disclosed in the KPI section; QLNFKSEC the quality of non-

financial KPIs disclosed in the KPI section; QLNFKREP the quality of non-financial KPIs disclosed in

the whole report; QLTKREP the aggregated quality of financial and non-financial KPIs disclosed in the

whole report.

Furthermore, the study compares KPI reporting quality scores across the sample period

to test whether the differences between these scores are significant. Table 15 indicates

that the means are not all equal, as the F value is 9.30. The multiple comparisons

between quality scores explore the differences between each pair of quality score

means. The findings presented in Table 15 show that there is an increasing trend in the

quality of KPI reporting between 2006 and 2010. However, there are significant

differences between the quality scores’ mean of 2006 and the quality scores’ means of

2008, 2009, and 2010, at a level of 5%. In addition, the results show a statistically

significant difference between the quality scores’ mean of 2007 and the quality scores’

mean of 2010. Thus, Table 48 Appendix (1) indicates that these findings are not

changed when non-financial KPIs disclosed - outside the KPI section - are considered.

Furthermore, Table 45 in Appendix (1) confirms the same results with respect to the

quality of financial KPI reporting. In addition, it shows that the difference between the

quality scores of 2006 and those of 2007 is significant at the 10% level.


69

Table 15 Anova test to compare KPI reporting quality across the sample period

QLTKSEC the aggregated quality of financial and non-financial KPIs disclosed in the KPI section.

On the other hand, Table 46 in Appendix (1) shows that significant differences exist

only between non-financial KPI reporting quality scores of 2006 and those of 2009, and

2010. Likewise the results discussed with regard to KPI reporting quantity differences,

quality score differences are mainly in relation to the quality scores of 2006. Thus, these

tests do not provide evidence that UK firms significantly extended KPI reporting

0.000 0.023 1.000 1.000 2010 .164002 .09124 .040377 .024489 0.000 0.252 1.000 2009 .139513 .066751 .015888 0.000 0.877 2008 .123626 .050863 0.163 2007 .072762 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QLTKSEC by year


Total 24.0038219 502 .047816378 Within groups 22.335639 498 .044850681Between groups 1.66818294 4 .417045735 9.30 0.0000 Source SS df MS F Prob > F Analysis of Variance

Total .56171643 .21866956 503 2010 .62449125 .15650629 102 2009 .60000241 .17932706 102 2008 .58411464 .19614196 102 2007 .5332513 .24024106 101 2006 .46048894 .27060419 96 year Mean Std. Dev. Freq. Summary of QLTKSEC


70

quality after the financial crisis in 2008. As concluded earlier, this might reflect firms’

focus on other disclosure types in this time period.

2.5.3.3 Quality of KPI reporting across industries

The study explores the variation in practice with respect to the quality of KPIs disclosed

across industries. Similar to the picture of KPI reporting in terms of quantity, it appears

that industries vary in the quality of KPI reporting. Table 16 indicates that the highest

level of KPI reporting in terms of quality is provided by the Basic Materials industry

either when considering KPIs disclosed outside the KPI section or not. The mean

(median) of QLTKSEC in Basic Materials firms is 0.459 (0.475), while the mean

(median) of QLTKREP is 0.461 (0.483). The same industry presents the highest quality

of financial KPI reporting with a mean (median) of 0.434 (0.471). However, the highest

quality of non-financial KPI reporting is shown in the Utilities industry with a mean

(median) of 0.436 (0.476) in the KPI section, and 0.461 (0.476) in the whole report.

On the other hand, firms in the Oil & Gas industry come at the bottom with regard to

KPI reporting quality even if KPIs disclosed outside the KPI section are considered or

not. Table 16 indicates that the mean (median) of QLTKSEC in this industry is 0.291

(0.321), while the mean (median) of QLTKREP is 0.294 (0.321). Oil & Gas firms also

provided the lowest level of financial KPI reporting in terms of quality with a mean

(median) of 0.289 (0.323). In turn, Healthcare firms show the lowest level of non-

financial KPI reporting in terms of quality among the sample industries with a mean

(median) of 0.122 (0.000).

It is worth mentioning that the statistics of the Oil & Gas and Utilities industries are

unique, as the levels of non-financial KPI reporting quality in these industries exceed

the corresponding figures for financial KPIs. In contrast to other industries, the high


71

levels of non-financial KPI reporting quality in the Oil & Gas and Utilities industries

result in an upwards trend in the total level of KPI reporting in terms of quality.

Table 16 Quality of KPI reporting across industries

Basic Materials


QLTKSEC 0.688 0.00 0.459 0.475 0.184 40

QLFKS 0.686 0.00 0.434 0.471 0.176 40

QLNFKSEC 0.786 0.00 0.355 0.429 0.321 40

QLNFKREP 0.818 0.00 0.374 0.443 0.323 40

QLTKREP 0.665 0.00 0.461 0.483 0.184 40

Consumer Goods


QLTKSEC 0.688 0.00 0.373 0.386 0.176 65

QLFKS 0.691 0.00 0.362 0.386 0.172 65

QLNFKSEC 0.714 0.00 0.227 0.143 0.247 65

QLNFKREP 0.714 0.00 0.272 0.286 0.254 65

QLTKREP 0.665 0.00 0.369 0.386 0.170 65

Consumer Services


QLTKSEC 0.582 0.00 0.328 0.353 0.158 107

QLFKS 0.583 0.00 0.320 0.339 0.161 107

QLNFKSEC 0.720 0.00 0.271 0.286 0.226 107

QLNFKREP 0.720 0.00 0.311 0.333 0.222 107

QLTKREP 0.582 0.00 0.343 0.357 0.155 107

Health Care


QLTKSEC 0.604 0.00 0.359 0.393 0.198 24

QLFKS 0.604 0.00 0.360 0.422 0.204 24

QLNFKSEC 0.429 0.00 0.122 0.000 0.166 24

QLNFKREP 0.492 0.00 0.218 0.286 0.199 24

QLTKREP 0.534 0.00 0.342 0.393 0.180 24

Industrials


QLTKSEC 0.658 0.00 0.384 0.408 0.148 143

QLFKS 0.632 0.00 0.355 0.375 0.158 143

QLNFKSEC 0.786 0.00 0.292 0.333 0.258 143

QLNFKREP 0.818 0.00 0.337 0.429 0.259 143

QLTKREP 0.665 0.00 0.397 0.417 0.146 143

Oil & Gas


QLTKSEC 0.612 0.00 0.291 0.321 0.215 54

QLFKS 0.627 0.00 0.289 0.323 0.217 54


72

QLNFKSEC 0.714 0.00 0.296 0.286 0.225 54

QLNFKREP 0.714 0.00 0.299 0.286 0.227 54

QLTKREP 0.612 0.00 0.294 0.321 0.216 54

Technology


QLTKSEC 0.582 0.00 0.342 0.324 0.144 40

QLFKS 0.593 0.00 0.320 0.300 0.163 40

QLNFKSEC 0.714 0.00 0.114 0.000 0.188 40

QLNFKREP 0.810 0.00 0.205 0.000 0.263 40

QLTKREP 0.633 0.00 0.356 0.373 0.150 40

Telecommunications


QLTKSEC 0.675 0.00 0.392 0.391 0.24 10

QLFKS 0.691 0.00 0.399 0.389 0.247 10

QLNFKSEC 0.571 0.00 0.260 0.229 0.276 10

QLNFKREP 0.671 0.00 0.280 0.254 0.298 10

QLTKREP 0.614 0.00 0.384 0.391 0.228 10

Utilities

Variable Max Min Mean Median SD N

QLTKSEC 0.608 0.00 0.407 0.406 0.163 20

QLFKS 0.575 0.00 0.370 0.375 0.144 20

QLNFKSEC 0.657 0.00 0.436 0.476 0.176 20

QLNFKREP 0.657 0.00 0.461 0.476 0.142 20

QLTKREP 0.608 0.00 0.427 0.406 0.133 20

QLTKSEC the aggregated quality of financial and non-financial KPIs disclosed in the KPI section;

QLFKS is the quality of financial KPIs disclosed in the KPI section; QLNFKSEC the quality of non-

financial KPIs disclosed in the KPI section; QLNFKREP the quality of non-financial KPIs disclosed in

the whole report; QLTKREP the aggregated quality of financial and non-financial KPIs disclosed in the

whole report.

To test whether the differences between industries in terms of quality scores are

significant,

Table 52 in Appendix (1) shows that the means are not all equal as the F value is

significant. A Bonferroni test conducts multiple comparisons between each pair of

quality scores. The results reported in Table 57 in Appendix (1) indicate that Basic

Material firms provide higher levels of disclosure quality, but the differences in terms

of quality scores with other industries are not significant. In contrast, it seems that Oil

& Gas provide KPI reporting at a lower level compared with other industries. However,

significant differences exist between scores in terms of quality in the case of Oil & Gas


73

firms and those of Basic Materials as well as Industrials. Table 58 in Appendix (1);

reports that these findings hold if KPIs disclosed outside the KPI section are considered.

With respect to the quality of financial KPI reporting, Table 54 in Appendix (1) reports

that Basic Materials firms show a higher scores in terms of quality compared with other

industries. However, all the differences are not significant, except those in the case of

Oil & Gas firms. Rather, Table 55 in Appendix (1) illustrates that Utilities’ firms report

higher levels of non-financial KPI reporting in terms of quality than other industries.

The differences are statistically significant compared with firms in the Consumer

Goods, Healthcare, and Basic Materials industries. In contrast, Technology firms

provide statistically significant and lower levels of non-financial KPI reporting in terms

of quality than other firms. The differences are mainly significant in respect to firms in

the Basic Materials, Consumer Services, Industrials, Oil & Gas, and Utilities industries.

2.5.4 Correlation between KPI reporting quantity and its quality

Descriptive statistics show that, to some extent, sample firms vary in terms of the

quantity and quality of KPI reporting. Thus, the current study also seeks to get an initial

indication on whether each dimension is to some extent related. Table 17 illustrates

Pearson’s correlation coefficients between KPI reporting quantity and quality proxies. It

appears that the correlation is positive and statistically significant between proxies that

are used to measure KPI reporting quantity or quality separately. This shows the

consistency among each group of measures in capturing the required information.

Table 17 Pearson correlation matrix


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*Significance at the 5% level or above.

QNTKSEC: the quantity of financial and non-financial KPIs disclosed in the KPI section; QLTKSEC:

the aggregated quality of financial and non-financial KPIs disclosed in the KPI section; QNFKS: the

quantity of financial KPIs disclosed in the KPI section; QLFKS: the aggregated quality score of financial

KPIs disclosed in KPI section; QNNFKSEC: the quantity of non-financial KPIs disclosed in the KPI

section; QLNFKSEC: the aggregated quality score of non-financial KPIs disclosed in the KPI section;

QNNFKREP: the quantity of non-financial KPIs disclosed in the whole report; QLNFKREP: the

aggregated quality score of non-financial KPIs disclosed in the whole report; ; QNTKREP: quantity of

financial and non-financial KPIs disclosed in the whole report; QLTKREP: the aggregated quality score

of financial and non-financial KPIs disclosed in the whole report. All variables are defined in Table 5.

In addition, a positive and statistically significant relationship is found between the

number of KPIs disclosed in the KPI section and their quality (ρ = 0.76). Hence, this

initial evidence might confirm the assumption of several empirical studies that use

quantity of information disclosed as a proxy for disclosure quality (e.g. Berretta and

Bozzolan, 2004; Mouselli and Hussainey, 2010). However, it is not possible to obtain

strong evidence with regard to this research question (Q4) at this early stage of the

analysis. Further investigation is needed to test this assumption in the next hypothesis.

2.6 Discussion and overall conclusion

The main objective of the current study is to explore the main features of KPI reporting

by UK firms. Therefore, a research instrument is first developed to measure the quantity

and to evaluate the quality of KPI disclosure. The quantity of KPI disclosure is

measured by counting the number of KPIs disclosed in the annual reports. The study

approach to measure KPI reporting quality is based upon a framework of a well

recognised regulatory body (the ASB, 2006), that aims at information usefulness.

Dependent Variables QNTKSEC QLTKSEC QNFKS QNNFKSEC QNNFKREP QNTKREP QLFKS QLNFKSEC QLNFKREP QLTKREP

QNTKSEC 1

QLTKSEC 0.7645* 1

QNFKS 0.8915* 0.7041* 1

QNNFKSEC 0.6945* 0.4302* 0.3399* 1

QNNFKREP 0.6321* 0.4659* 0.3575* 0.7972* 1

QNTKREP 0.9499* 0.7658* 0.8655* 0.6192* 0.7458* 1

QLFKS 0.7328* 0.9190* 0.7840* 0.3270* 0.3820* 0.7410* 1

QLNFKSEC 0.5963* 0.5292* 0.3545* 0.8348* 0.6443* 0.5286* 0.4220* 1

QLNFKREP 0.5697* 0.5494* 0.3835* 0.6840* 0.8160* 0.6407* 0.4531* 0.8354* 1

QLTKREP 0.7291* 0.9614* 0.6740* 0.4040* 0.4944* 0.7701* 0.8838* 0.5052* 0.5959* 1


75

Arguably, firms with a high alignment to this guidance would provide KPI information

that is relevant, has a forward-looking orientation, is comprehensive and

understandable, is balanced and neutral, complements and supplements financial

statements, is verifiable and comparable over time. As the index focus is on assessing

the quality of the information unit, it would be relevant to measure the quality of

different types of information disclosed (e.g. risk reporting). The approach followed

aimed at measuring disclosure quality does not allow a high degree of subjective

judgment. This adds to the reliability of the measure, which has been assured by

conducting a pilot study.

The study focuses on exploring KPI reporting practices in FTSE 350 non-financial UK

firms. The study sample is identified as 103 firms with 515 annual reports published

between 2006 and 2010. A research instrument is used to quantify KPI reporting and to

assign quality scores to the sample firms.

The analysis of firms’ practices indicates that the majority of UK firms specify a section

of the business review in order to present KPIs, or at least they make a reference to the

part in which they discuss KPI information. However, the nature of the regulations

regarding KPI reporting appears to give the directors a large degree of discretion when

it comes to controlling such disclosures. Some firms are unconcerned with providing

any KPI disclosures, especially those related to non-financial KPI information, while

others provide more KPIs in their annual reports, with a continuous increase in

reporting quality across the sample period (2006-2010).

Whereas, the most popular financial KPIs disclosed by the sample firms are revenues

followed by underlying earnings per share and free cash flow, the most common non-

financial KPIs disclosed are accident incident rate, employee turnover\ retention,


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accident numbers. Descriptive analyses show that firms gradually increased the number

of KPIs disclosed across the period (2006-2010). The significant differences between

the quantity scores’ mean of 2006 and the quantity scores’ means of 2008, 2009, and

2010 might reflect the normal effect of a learning curve with regard to KPI reporting in

practice.

Although the median number of non-financial KPIs has increased from 0 in 2006 to 2 in

2010, it is observed that the level of these KPIs is relatively low. This result is in line

with the ASB (2007) review of companies’ narrative reporting practices. Hence, the

number of non-financial KPIs disclosed could not cover all the different aspects of

performance.

With respect to KPI reporting quality, a remarkably low level of KPI reporting quality

is observed. That level is slightly improved if the KPIs disclosed outside the KPI

section are considered. Similar to the picture of reporting quantity, the analysis

indicates the increasing trend in quality levels across the sample period. The level of

financial KPI disclosure quality is always larger than the corresponding figure for non-

financial KPIs during the period 2006-2010.

Generally, the low level of reporting quality could be explained by firms’ weak

performance with regard to most of the individual attributes that have been suggested

by the ASB (2006) guidance in order to achieve KPI reporting quality. Firms place

emphasis only on providing appropriate definitions and in quantifying KPI data. In

contrast, they do not pay a great deal of attention to the other attributes of KPI reporting

quality. This raises the question about the need to introduce clear guidelines and

benchmarks concerning KPI disclosures. Furthermore, additional mechanisms might be

required from regulators to make UK firms more compliant.

The findings show that there are no significant differences between KPI reporting


77

scores before and after the financial crisis of 2008. Hence, the results do not provide

evidence suggesting that the financial crisis affected either KPI reporting quantity or its

quality. It appears that companies did not use KPI reporting to communicate with

annual reports users with respect to the impact of the financial crisis on the business

during this period.

The analyses provide a full picture with regard to the variation between UK industries

in terms of KPI reporting. It is believed that KPI reporting is influenced by different

types of businesses that have different and unique value creation activities. This is in

line with different disclosure theories. For instance, signalling theory suggests that

companies in the same industry would follow the same disclosure practices to show that

they are not hiding any bad news (Craven and Marston, 1999; Oyelere et al., 2003). On

the other hand, different industries are subject to different political costs, which result in

different disclosure practices across different industries (Ball and Foster, 1982).

For instance, Utilities sector companies such as electric, gas and water providers usually

rely on significant investments and debts. These firms also place stress on non-financial

KPI reporting which could be explained by their aim to demonstrate their ability to

achieve sustained growth. Therefore, Utilities firms disclose the largest amount of KPIs

to show their ability to meet the different needs of their stakeholders (e.g. creditors,

employees, customers, suppliers, and government authorities). This is in line with

stakeholder theory; Utilities companies try to use KPI information as a vehicle to ensure

that these stakeholders are informed, even if they do not use this information (Deegan

and Unerman, 2006).

On the other hand, Healthcare firms present the lowest number of KPIs in general.

Furthermore, Technology firms provide the lowest number of non-financial KPIs, with


78

a significantly lower degree of reporting quality. Arguably, these industries should

increase their focus on KPI reporting. Previous research has documented that these

industries are characterised as having intensive intangibles. Therefore, they should rely

on non-financial information, as investors find the financial information disclosed in the

financial statement irrelevant (Amir and Lev, 1996).

Likewise, Oil & Gas firms provide low level of KPI reporting quality. The activities of

these companies have different impacts on the environment. This could also affect their

current and future financial performance. Shareholders might overestimate the potential

effects of such activities. Thus, it appears that Oil & Gas companies avoid the negative

consequences of high quality KPI disclosure. In turn, in accordance with political cost

theory, it can also be argued that these companies involved in vulnerable activities are

under the public eye, and hence they control their disclosures to alleviate political costs

related to their activities (Oyelere et al., 2003).

These findings indicate that industries vary in terms of KPI reporting. Therefore, one

might argue that the industry could affect reporting quantity and quality, and hence,

should be considered while examining the factors that drive KPI reporting. In addition,

the variation between firms in terms of KPI reporting could be a good motive to study

the economic impact of KPI reporting.

This study is the first part of an integrated research project that aims to identify factors

affecting KPI reporting, as well as investigating the impact on firm value. In general,

there is limited empirical evidence on firms’ practices with regard to KPI reporting in

the UK. The current study contributes to the existing literature by its comprehensive

analysis of KPI disclosures in the UK setting. It provides an answer to the first research


79

question (Q1) with regard to exploring the main characteristics of KPI reporting on the

part of UK firms.

Finally, the research approach is to make a distinction between disclosure quantity and

its quality. This distinction helps to answer Q4 of the research questions about the

validity of using quantity of disclosure as a proxy for its quality in accounting studies.

Descriptive results suggest a significant and positive relationship between KPI reporting

quantity and its quality (ρ = 0.76). On the other hand, the comparison between

industries in terms of KPI reporting illustrates that the industries with the highest

quantity of KPI disclosure do not appear to be the best in terms of KPI disclosure

quality. Thus, Utilities firms were the highest in terms of KPI reporting quantity, but

they just show the highest level of non-financial KPI reporting quality. In contrast, the

highest level of KPI reporting quality is provided by the Basic Materials industry.

Furthermore, Healthcare firms provide the lowest level of KPI reporting quantity, but

Oil & Gas and Technology firms provide the lowest level of total KPI reporting quality.

Nevertheless, that is not enough to provide evidence of the relationship between

quantity and quality of KPI reporting in this early stage of analysis. Thus, these initial

findings represent a good motive to conduct further analyses. These analyses could

provide empirical evidence on whether the quantity and the quality of KPIs might be

used interchangeably.

CHAPTER THREE: THE DETERMINANTS OF KPI REPORTING IN THE UK

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Chapter 3 - The determinants of KPI reporting in the UK

3.1 Overview

As mentioned earlier, the UK Company Act (CA) of 2006, in accordance with the

European Union (EU) Accounts Modernisation Directive (2003), requires all

companies – with the exception of small ones - to publish key performance indicators

(KPIs). However, these regulations allow company directors to report on KPIs which

can be considered as necessary and appropriate when it comes to analysing their

companies’ performance. Thus, it can be argued that the KPI reporting extent would be

derived from directors’ motivation to voluntarily disclose more information on KPIs.

The aim of this chapter is to provide answers to research questions Q2 and Q4. It

explores which factors affect the level of quantity and quality of KPI reporting in the

UK (Q2). It also links the findings to question the validity of using quantity of

disclosure as a proxy for its quality in accounting studies (Q4). The study provides

evidence on the CG mechanisms that influence the quantity and quality of KPI

reporting. In particular, it contributes to the existing but limited studies on the

association between directors’ compensation and corporate disclosure. Moreover, this

study shows that that the quantity and the quality of KPI disclosure are not identically

derived from the same factors.

The remainder of this chapter is organised as follows: section 3.2 provides a theoretical

basis for explaining the managerial incentives to control corporate disclosure, and

identifies factors affecting this disclosure. Section 3.3 presents the potential

determinants of KPI reporting according to the previous literature. Section 3.4 develops

the hypotheses of the study. Section 3.5 illustrates the data, provides the descriptive

results, and introduces the regression models. Section 3.6 contains the main analysis; it

includes the basic procedures to ensure a high degree of confidence in the results. In


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addition, it reports the empirical findings with regard to the determinants with regard to

reporting on KPIs sub-categories. Section 3.7 provides a discussion of the empirical

results. Section 3.8 includes further analyses that consider KPIs reported outside the

KPI section to show their effect on the main findings. Finally, a conclusion of the

current study is provided in section 3.8.

3.2 Theories that explain KPI reporting

Generally, the literature presents many theories in order to explain the variation

between entities in terms of their level of disclosure.15 Various theories have been

developed to explain managers’ incentives for disclosure. However, there is no general

or comprehensive disclosure theory that can be applied (Verrecchia, 2001). Apparently,

each theory explains different aspects of corporate disclosure, or it looks at this

phenomenon from different perspectives (Al-Htaybat, 2005). Therefore, it was argued

that several theories could be used in an integrated framework to provide an

explanation for managerial incentives which affect corporate disclosure (Al-Htaybat,

2005). The objective of this section is to present the main theories that provide a

fundamental background with regard to the determinants of voluntary disclosure. These

theories include: agency theory, signalling theory, capital need theory, political cost

theory, stakeholder theory, and information costs theory.

3.2.1 Agency theory

Agency theory is widely used to explain managers’ incentives for providing voluntary

disclosure. Jensen and Meckling (1976) pointed to the agency relationship that arises

from the separation of ownership and control of public companies. Based on this

situation, a contractual relationship is established between owners (the principals) and

managers (the agents) in order for the latter to perform some services on the owners’

15

See for example: Spence (1973); Jensen and Meckling (1976); Hughes (1986); Watts and Zimmerman

(1990); Cooke (1992); Healy and Palepu (2001); Al-Htaybat (2005).


82

behalf. According to agency theory, a conflict of interests can exist when the agent

(managers) acts in his own interest.

Jensen and Meckling (1976) identified three types of agency costs which are associated

with the agency problem. First: monitoring costs that are incurred by giving certain

incentives to the agent which motivates him to act in the principal’s interests. Second:

bonding costs which occur when the agent uses additional resources to make sure that

his actions will not be against the principal’s interests. Third: the residual loss that

results from the reduction in the principal’s welfare. 16

On the other hand, another conflict could exist between corporate managers and their

lenders, as the managers can take actions that transfer wealth from the debt-holders.

Healy and Palepu (2001) argued that an information asymmetry problem usually exists

as managers have corporate information which is not available to the various

stakeholders. Voluntary disclosure is a means used by insiders to reduce information

asymmetry through disseminating the information they have to outsiders (Lakhal,

2005). Thus, incentives schemes and contracts are used to encourage managers to

provide adequate information (Healy and Palepu, 2001).

Consistent with this conjecture, corporate directors voluntarily extend the limit of

information disclosed to reduce agency problems. Hence, they present information that

proves that they are acting in the interests of the shareholders and the debt holders.

Therefore, it can be argued that the provision of KPI information by the management

(the insider who has this kind of information) to the investors and debt-holders (the

16

Managers display a tendency towards maximizing their own wealth and, hence, this is more likely to

lead to a reduction in shareholder value in the long run. For instance, they supplement their salaries with

many perquisites (Solomon, 2010). Schleifer and Vishny (1997) showed that managers benefit from this

by building their own empires, enjoying perks, insider trading, inappropriate investments and

management entrenchment.


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outsiders who usually do not have this information) would reduce the information

asymmetry problem.

Agency theory provides a framework for explaining disclosure practices on the part of

different firms. It was claimed that agency theory predicts that agency costs are

associated with different corporate characteristics such as size, leverage and listing

status (Watson et al., 2002). Furthermore, corporate governance (CG) mechanisms are

introduced by shareholders in order to ensure that managers’ actions are aligned with

shareholder’s interests. As a result, many empirical studies use firm characteristics and

CG mechanisms as determinants of corporate disclosure; in accordance with agency

theory assumptions (e.g. Cooke, 1992; Lang and Lundholm, 1993, Ghazali and

Weetman, 2006; Ho and Wong, 2001; Haniffa and Cooke, 2002; Ajinkya et al., 2005;

Tauringana and Mangena, 2009; Hussainey and Al-Najjar, 2011).

3.2.2 Signalling theory

Signalling theory was developed by Akerlof (1970) to explain the impact of the

interaction of quality differences and uncertainty on the institution of the labour market

in the U.S. This theory explains the impact on market equilibrium in the event of

information asymmetry in the market. Despite the fact that this model has been used in

the employment market where a job seeker signals his/her quality to a prospective

employer, Spence (1973) argued that this model can be generalised to other settings.

Basically, signalling theory can be applied in the event of information asymmetry, and

hence this problem may be reduced when the party who has more information signals it

to other interested parties (Morris, 1987). Therefore, this theory was used to explain

managers’ incentives to disclose more information in financial reports (e.g. Hughes,

1986; Haniffa and Cooke, 2002). Managers have to disclose adequate information in

the financial statements in order to convey specific signals to potential users.


84

According to signalling theory, good performance will motivate the managers to

disclose more information in order to distinguish themselves from others with lower

quality performance (Morris, 1987). On the other hand, investors do not appreciate

being surprised by bad news, and financial analysts may choose not to follow stocks of

firms whose managers have a reputation for withholding bad news (Skinner, 1994).

Signalling theory can be used to explain managers’ incentives to improve KPI reporting

in order to convey particular signals to the stakeholders.17 One can expect that they will

report more good news through KPI disclosures, in order to signal themselves as high

quality managers in the work market. This is of particular relevance in the present

study. For instance, it leads to the assumption of a relationship between directors’

compensation and KPI reporting. Moreover, directors attempt to send good signals to

the investors and debt holders. Sending these signals to debt-holders may enable the

company to avoid any additional restrictive covenants in debt contracts. Therefore, it is

expected that the intent to obtain finance would affect KPI reporting. For investors, this

kind of communication is credible, because managers sending fraudulent signals will be

penalised (Hughes, 1986). On the other hand, managers could send bad news via KPI

reporting in order to avoid the consequences of having a reputation for withholding bad

news (Skinner, 1994).

3.2.3 Capital need theory

Capital need theory assumes a relationship between corporate disclosure and the

entrance into the local / international capital market. Corporate managers have to

respond to the demand for information in order to obtain funds or to raise capital as

cheaply as possible (Choi, 1973). Cooke (1993) provided another reason which

supports the capital-need hypothesis. He argued that listed and multiple listed

17

It is argued that signalling theory would support the disclosure of certain types of ratios (some of them

are considered as KPIs such as investment, profitability and efficiency ratios) by those companies

wishing to highlight favourable aspects of their performance (Watson et al., 2002).


85

companies’ aim to achieve overseas countries’ acceptance. Therefore, they may

increase social responsibility disclosures to show that their business acts in a

responsible manner.

A high level of corporate disclosure would result in a clearer understanding of business

strategies. Thus, managers will be able to mitigate information asymmetry through

voluntary disclosure (Cooke, 1993; Healy and Palepu, 2001). Accordingly, one can

argue that managers could use reporting on financial and non-financial KPIs in order to

provide investors with a complete picture regarding corporate strategy and

performance. Consequently, this would reduce perceived uncertainties which, in turn,

could help firms raise capital as cheaply as possible, either from local or international

markets.

3.2.4 Political cost theory

Besso and Kumar (2007) stated that companies direct their reporting towards key

stakeholders who affect the activity of the company. Political cost theory was driven by

Watts and Zimmerman’s (1978) research into positive accounting theory to explain the

determinants of managers’ choices in an accounting practice context. They argued that

politicians’ decisions could affect corporate wealth through certain restrictions or

regulations (e.g. tax laws). Hence, if a company is subject to potential wealth transfers,

its management will be motivated to adopt several accounting procedures in order to

reduce the reported earnings (Watts and Zimmerman, 1978).

Disclosure studies usually use this theory to investigate the influence of corporate size

and profitability (e.g. Wong, 1988). Large or highly profitable companies attract

political scrutiny, and therefore their managers are motivated to reduce the political cost

by extending the extent of voluntary disclosure (Al-Htaybat, 2005). Similarly, Gazali

(2004) claimed that the industry variable could be associated with political cost levels.


86

For instance, oil and gas companies have more incentives than others to disclose more

information to avoid future regulatory costs.

In this context, KPI reporting could be used as a tool to avoid political costs, especially

for companies in the public eye. For instance, managers will be motivated to provide

more KPIs related to corporate social responsibility in order to alleviate political

pressure.

Moreover, it is most likely that big and / or highly profitable companies would stay

away from political costs through presenting more KPIs. Those KPIs would focus on

other aspects of its performance rather than concentrating only on earnings’ figures.

3.2.5 Stakeholder theory

Clarkson (1995, p. 106) defines stakeholders as ‘Persons or groups that have, or claim,

ownership, rights, or interests in a corporation and its activities, past, present, or

future. Such claimed rights or interests are the result of transactions with, or actions

taken by, the corporation, and may be legal or moral, individual or collective.’

The idea behind stakeholder theory is that the organisation is a part of a broader social

system, and it affects and is impacted on by other groups within society (Deegan et al.,

2002). All stakeholders have the right to be informed about the organisation’s influence

on them, even if they choose not to use the information, and even if they have no direct

impact on the organisation’s survival (Deegan and Unerman, 2006). Therefore,

managers should take into consideration the need to report to other groups who have a

stake in the company (e.g. creditors, employees, customers, suppliers, government

authorities).

Taking into consideration the conflict of interest between stakeholders, organizations

tend to develop and improve their financial reporting. It is important for them to have


87

stakeholder dialogue in order to build and manage effective stakeholder relationships

(Boesso and Kumar, 2009). However, the organisation most often gives the priority to

the demands of stakeholder(s) that are considered to be powerful (Deegan and

Unerman, 2006).18

Successful managers have to arrive at a balance when satisfying the needs of different

powerful stakeholders (Deegan and Unerman, 2006). Corporate reporting could

highlight corporate social responsibility activities to avoid accountability concerns. In

addition, voluntary disclosure could be employed to satisfy stakeholders’ expectations

about corporate performance with regard to financial, social, and environmental

aspects.

Ulmann (1985) argued that firms use social disclosures in order to manage its

relationships with their stakeholders. He suggested that social disclosure is a function of

three dimensions: stakeholders’ power, strategic posture and economic performance.

These dimensions are found to be related to both social disclosure levels (Robert, 1992)

and environmental disclosures (Chan and Kent, 2003).

Given that non-financial KPI reporting covers social and environmental perspectives of

corporate performance, managers are expected to increase the quantity and quality of

KPI disclosures to manage and achieve better communications with various

stakeholders. In line with stakeholder theory, it is expected that some firm’s

characteristics could affect KPI reporting. For instance, highly leveraged firms would

provide more financial KPIs to meet the demands of debt-holders. On the other hand,

firms with a concentrated ownership will not be motivated to disclose more KPIs in

general or non-financial KPIs in particular.

18

It was argued that whenever stakeholders have a conflict of interests, corporations will prefer to

provide information to those stakeholders deemed to be more critical for their survival (Neu et al., 1998).


88

3.2.6 Information costs theory

Managers’ decisions to provide voluntary information will be based on cost-benefit

analysis. It was argued that corporate disclosure will be made as long as its benefits

exceed its costs (e.g. collection, supervision, legal fees and auditing costs) (Cooke,

1992; Heitzman et al., 2010). However, it is not obvious if all benefits and costs - either

neutrally or by weighting - are taken into account when it comes to determining

disclosure levels (Beattie and Smith, 2012).

In general, financial disclosure costs can be classified into direct and indirect costs

(Foster, 1986 cited by Al-Htaybat, 2005):

- Direct costs include costs related to collecting, preparing, processing and

auditing financial information.

- Indirect costs include competitive disadvantage costs arising from the use of

disclosed information by competitors, litigation costs when users claim for

using incorrect financial information, and political costs like taxes required by

the government.

Accordingly, additional KPI disclosures will be provided after considering its costs and

benefits. However, there is no study that discusses the benefits of KPI reporting.

Regarding costs, performance indicators are used to assess changes in performance, and

hence most KPI information is already available without any incremental costs.

Additionally, UK companies can avoid competitive disadvantage costs by considering

disclosure of some KPIs that are seriously harmful to company’s interests.

3.2.7 Summary of relevant theories

In summary, section 3.2 reviews the relevant theories which explain managers’


89

incentives to voluntarily provide a wide range of information. Managers may have

many motives to control disclosure levels, and thus variations between companies with

regard to disclosure level will exist. Each theory looks at the disclosure phenomenon

from a different perspective. In this context, Gray et al. (1995) stated that different

theoretical perspectives need to be seen as sources of explanation of different factors, at

different levels of resolution, not as competitors for explanation. In line with this

conclusion, Beattie and Smith (2012) documented that adopting more than economic

and managerial theories will enable us to explain manager’s incentives to voluntarily

disclose information. This range of theories helps us to focus on different aspects of

corporate behaviour.

KPI reporting incorporates financial and non-financial information which cover

operating, social, and environmental issues. Previous research has found that, in such a

setting, the incentives behind each type of disclosure and its importance would vary

between disclosure topics or within its content (Kothari et al., 2009; Beattie and Smith,

2012). Therefore, it will not be appropriate to use a specific theory, on its own, as a

logical base for KPI reporting in practice. Incentive theories for KPI reporting could

include agency theory, signalling theory, in addition to capital need theory. For

instance, in accordance with agency theory, managers will be motivated to present

particular performance indicators that prove that they are acting in the interests of the

shareholders. On the other hand, one can argue that this is driven by their need to signal

themselves as high quality managers (signalling theory). In this regard, Morris (1987)

argued that there is a consistency between both agency theory and signalling theory. He

suggested that a combination of them would provide a better prediction of accounting

choices. The same approach was also adopted by Watson et al. (2002) to get greater

insight into managers’ motives to voluntarily disclose accounting ratios in the UK.

Hence, it is proposed that we consider agency theory and signalling theory as being


90

complementary. Additionally it will be acceptable to explain KPI reporting in terms of

managers’ response to investors’ demand for information, which would be helpful with

regard to raising capital at the lowest possible cost (capital need and stakeholder

theories). Arguably, this approach will be relevant as there is a variety of variables that

are suggested in the literature as explanatory factors for corporate disclosure. These

variables that represent determinants of disclosure can be explained by more than one

theory to obtain greater insights into the motivations for controlling the quantity and

quality of KPI disclosures.

3.3 Previous literature

This section highlights the findings of previous researchers who have examined the

determinants of corporate disclosure in general, and KPI reporting in particular. As

mentioned before, KPI reporting is more likely to be voluntary in nature. Previous

research provides evidence of the relationship between corporate disclosure and firm

characteristics which have been used as proxies for a variety of theories discussed in

the previous section (e.g. Cooke, 1989; Wallace et al., 1994; Ahmed and Courtis,

1999). In addition, many studies have investigated the impact of corporate governance

(CG) attributes on corporate voluntary disclosure (CVD) as a whole (e.g. Ho and

Wong, 2001; Wang et al., 2008). Furthermore, CG effect on CVD subcategories has

been examined in the previous literature, such as forward-looking information (e.g.

Wang and Hussainey, 2013; Hussainey and Al-Najjar 2011), online reporting (e.g.

Abdelsalam and Street, 2007; Aly et al., 2010), and intellectual capital disclosure (e.g.

Li et al., 2008).

In particular, there have been a limited number of studies that have investigated the

determinants of KPI disclosure in annual reports. Boesso and Kumar (2007) examined

the factors driving voluntary disclosure practices. They employed content analysis to


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measure the quantity and quality of KPIs provided in the Management Discussion &

Analysis (MD&A) sections of the annual reports of 72 US and Italian companies during

2002. A list of 42 KPIs was determined with regard to their importance to stakeholder’s

communication needs. The quantity score was calculated based on the number of

sentences which contained any information concerning these KPIs. The quality score

was derived by giving a higher weight to KPI sentences which included quantitative,

non-financial and forward-looking information about KPIs. This study found that size,

and to some extent the industry concerned, have an impact on the quantity and quality

of KPI disclosures. Business complexity, instability and volatility were found to have

an influence on KPI reporting quantity rather than on its quality. However, the study

found that KPI reporting is not derived from CG or from intangibles. This study is a

good attempt to consider both quantity and quality of KPI reporting in the US and Italy.

However, the study suffers from several limitations. It is based on a small number of

observations across two different countries in one year. Moreover, the quality index

does not consider other qualitative characteristics of KPI information (e.g. the ability to

understand its content and to compare results across years). Furthermore, the study does

not control for many factors affecting CVD (e.g. CG mechanisms19 and ownership

structure).

Giunta et al. (2008) examined the quantity and quality of financial KPI reporting in the

period 2004-2006 on the part of 49 medium sized Italian companies. They employed

content analysis to capture the number of indicators published by each firm within the

period, and then they classified these KPIs in terms of their role in assessing the

development, profitability, and solvency of the firm. They measured KPI quality based

on the presence/absence of 10 qualitative aspects. These aspects were then grouped in

accordance with the four general dimensions presented by IASB (2005), which are

19

The study only used the percentage of independent directors in the board as a proxy for CG structure in

the company.


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relevance, understandability, reliability, and comparability. The quality scores were

derived by calculating the mean among the four dimensions. Their results showed the

low quantity and quality of financial KPI reporting. These results support the call for

regulating narrative disclosure in MC in general, and KPI reporting specifically.

Nonetheless, the main limitation of this paper is its reliance on a relatively small sample

comprising medium sized companies in an Italian setting. It also focused on only one

type of KPI (financial KPIs).

Tauringana and Mangena (2009) examined the extent of KPI reporting and the factors

affecting its level before and after the introduction of a business review. The results

suggested that the introduction of the business review had a significant impact upon

KPI reporting in the media sector. Additionally, the study showed that proportion of

Non-Executive Directors (NED), company size, profitability and gearing are associated

with the extent of KPI reporting in those companies. Although this study is the first to

explore the determinants of KPI reporting in UK, its results could not be generalised

because of its focus on media companies with only 32 companies as a sample.

Furthermore, the study neither analysed the quality of KPIs disclosed, nor examined its

determinants in this sector.

The UK Government seeks to improve communication between companies and the

users of information. Despite the variety of information provided within KPI

disclosures, previous studies have not provided empirical evidence showing what drives

UK companies to control the amount of KPI disclosed and its reporting quality. The

present study addresses this gap left by disclosure studies. Arguably, the study can help

to improve the dialogue between companies and shareholders, taking into consideration

the actual KPI reporting practices in the UK.


93

3.4 Research hypotheses

Exploring the determinants of KPI reporting quantity apart from its quality is the main

objective of the analyses in this study. Hence, it would provide answers to research

question (Q2). In addition, it helps to examine whether the quantity and quality of KPI

disclosure are derived from the same determinants. Hence, it would provide answers to

research question (Q4).

In fact, CG mechanisms are introduced - based on the agency theory framework - to

mitigate managers’ opportunistic behaviours and reduce information asymmetry. CG

mechanisms should facilitate corporate monitoring as they lead to an improvement in

companies’ internal control, and consequently extend disclosure levels (Leftwich et al.,

1981; Welker, 1995; Ho and Wong, 2001). Accordingly, it could be predicted that CG

mechanisms could improve KPI reporting.

Thus, this study takes into consideration most of the CG mechanisms as potential

determinants of KPI reporting. The Organisation for Economic Co-operation and

Development (OECD) indicated that corporate governance involves a set of

relationships between a company’s management, its board, its shareholders and other

stakeholders. It provides the structure through which the objectives of the company are

set, and the means by which attaining those objectives and monitoring performance are

determined (OECD, 2004). Ensuring timely and accurate disclosure regarding the

financial situation, performance, ownership and governance of the company is

considered as one of the main principles of corporate governance (OECD, 2004).

Schleifer and Vishny (1997) stated that providing a degree of confidence to the

suppliers of finance is the aim of CG. However, CG involves not only this objective but

also has effects on other stakeholders. The Cadbury Report (1992) in the UK refers to

the wider interests of corporate governance that intend to achieve a balance between


94

economic and social goals, and between individual and communal goals. The presence

of an effective CG system for each individual company across an economy helps to use

resources more efficiently, and therefore maintain sustainable economic growth for the

economy (OECD, 2004).

It is noted that most of the previous research has focused on CG mechanisms that are

related to the monitoring role of corporate board of directors. Hence, it has tested the

influence of the board and its sub-committee characteristics on the amount of

information disclosed. For instance, several studies examined the impact of board size,

board independence, members’ experience, role duality, board activity, board

committees, audit committee size, the experience of audit committee members, and

audit committee meetings (e.g. Forker, 1992; Ho and Wong, 2001; Haniffa and Cooke,

2002; Ajinkya et al., 2005; Tauringana and Mangena, 2009; Hussainey and Al-Najjar,

2011). Additionally, the impact of ownership structure has been examined to

understand managers’ incentives to extend corporate disclosure (e.g. Lakhal, 2005;

Wang and Hussainey, 2013).

The present study builds on the previous literature in order to develop and form the

hypotheses of the present study. After controlling for firm characteristics effects, the

current study suggests CG mechanisms as the main drivers for both quantity and quality

of KPI reporting. In order to obtain more insight into managers’ incentives to control

disclosures, this study examines the impact of directors’ compensation, and their plans

to get finance from different sources on KPI quantity and quality.

In particular, the proposed KPI reporting determinants include - in addition to firm

characteristics - directors’ compensation, board size, board composition, board

meetings, role duality, audit committee size (AC) size, AC meetings, managerial


95

ownership, major shareholding, and the issuance of shares, bonds and loans.

To facilitate forming and testing the hypotheses, these determinants can be classified

into the following categories: 3.4.1 Directors’ compensation, 3.4.2 Board

characteristics, 3.4.3 Audit committee characteristics, 3.4.4 Ownership structure, 3.4.5

Capital need variables, and 3.4.6 Firm characteristics variables as controls.20

3.4.1 Directors’ compensation

Directors’ compensation and their interests in company shares may be sufficient

incentives for controlling KPI reporting. Because of the separation of ownership and

control, there is a need to monitor managers’ actions (Jensen and Meckling, 1976).

Directors’ compensation works as an efficient corporate governance mechanism. This

mechanism aims to align the directors’ and the shareholders’ interests. Previous studies

find that directors’ remuneration plans have an impact on CVD (Aboody and Kasznic,

2000; Nagar et al., 2003; Grey et al., 2012). For instance, Aboody and Kasznic (2000)

found that top executives take opportunistic disclosure decisions in order to affect their

stock option compensation. Thus, they tend to disseminate bad news and delay good

news around stock option award times. However, previous research did not examine

how the extent and quality of CVD are affected by the average compensation for both

categories of directors. Consistent with agency theory, firms with higher directors’

compensation can mitigate the agency problem. As directors of these companies would

20

The main objective of the regulatory bodies - according to their theoretical frameworks - is to maintain

the high quality of information disclosed. Hence, previous studies originally aimed at studying the

determinants and consequences of corporate disclosure relying on the differences between firms in

disclosure quality. However, these studies used disclosure quantity to measure disclosure quality.

Therefore, one can argue that these theories and principles - followed in the previous literature - can

serve to study the quantity as well as the quality of KPI reporting. Furthermore, the relatively high

correlation which is found between both KPI reporting quantity proxies and KPI reporting quality

proxies in Table 17 in the previous study (ρ = 0.76), is another motivation to test whether or not they are

substitutes. Consequently, the same hypotheses are developed for KPI reporting quantity and quality

either in examining their determinants or their impact on firm value. Arguably, testing these shared

hypotheses empirically in each study would indicate whether quantity and quality of KPI reporting have

identical\different drivers\impacts. Hence, this would help us to find whether or not the quantity of KPI

reporting could work as a proxy for its quality.


96

tend to disclose their private information, a lower degree of information asymmetry

between directors and shareholders might be expected. Hence, this may improve the

quantity and quality of KPI reporting.

In line with this argument, signalling theory provides another explanation with regard

to compensation influence on corporate disclosure. This is because executive and non-

executive directors have incentives to improve KPI reporting in order to show their

impact on company’s performance and hence retain their high levels of compensation.

Their objective may also be to signal themselves as high quality managers in the

employment market. Therefore, these two hypotheses are formulated:

H1. A positive association exists between executive compensation and KPI reporting

quantity\quality.

H2. A positive association exists between non-executive compensation and KPI

reporting quantity\ quality.

3.4.2 Board characteristics

This group of variables is concerning with the board characteristics that may enhance\

alleviate the monitoring role of the board. These variables include: board size, board

composition, board meetings, and role duality.

3.4.2.1 Board size

The board of directors represents the total number of executive and non-executive

directors on the board. It plays an important role in the corporate governance of

publicly listed companies. Healy and Palepu (2001) stated that electing a board of

directors that acts on behalf of investors, is an efficient mechanism that affects

mangers’ voluntary disclosure decisions and controls the agency problem. However, it

was claimed that UK boards have a much weaker monitoring role as a result of soft CG


97

regulations in the UK, which allow firms to choose the board size that is most

appropriate for their own needs (Guest, 2008). Wang and Hussainey (2013) claimed

that larger boards’ effectiveness is negatively affected by the presence of problems

regarding communication and coordination.

In turn, some studies have indicated that larger boards incorporate a variety of expertise

which results in greater effectiveness in terms of the boards’ monitoring role (e.g. Singh

et al., 2004, Abdel Fattah, 2007). One can argue that directors serving on larger boards

would have more incentive to signal their role in performance improvement to various

parties. It was argued that talented managers are motivated by their desire to signal

themselves to make voluntary disclosures (Graham et al., 2005). Furthermore, it was

reported that this incentive is more important for managers of smaller and high growth

firms (Graham et al., 2005). Thus, based on signalling theory, a positive association

between board size and KPI reporting could be expected.

Some previous studies found a positive association between board size and voluntary

disclosure (e.g. Laksamana, 2008; Hussainey and Al-Najjar, 2011). Others found there

to be no significant impact in terms of board size on corporate disclosure (e.g. Lakhal,

2005; Cheng and Courtenay, 2006).

Based on these mixed results, the fourth hypothesis is formulated as:

H3. A significant relationship exists between KPI reporting quantity\ quality and board

size.

3.4.2.2 Board composition

Board composition refers to the proportion of non-executive directors (NEDs) on the

board. Non-executive directors are expected to provide independent advice to executive

directors. Boards with a higher proportion of non-executive directors are expected to be


98

more effective in performing a monitoring role, and thereby having a positive effect on

accounting reporting quality, as they may aim to signal their competence to potential

employers (Fama and Jensen, 1983).

Previous studies which have examined the relationship between board composition and

disclosure have provided mixed findings. Some studies found no statistically significant

association between them (e.g. Ho and Wong, 2001; Haniffa and Cooke, 2002;

Mangena and Pike, 2005; Lakhal, 2005). However, Tauringana and Mangena (2009)

found that the proportion of NEDs is associated negatively with KPI reporting. On the

other hand, a positive relationship has been reported between the proportion of NEDs

and the level of corporate disclosure in many studies (e.g. Forker, 1992; Cheng and

Courtenay, 2006, Abraham and Cox, 2007; Li et al., 2008; Laksamana, 2008; Wang

and Hussainey, 2013). Following these studies, a stronger monitoring role would be

expected from boards with a higher proportion of non-executive directors. Hence, a

positive relationship between the proportion of non-executive directors on the board

and KPI reporting will be hypothesised:

H4. There is a positive relationship between board composition and KPI reporting

quantity\ quality.

3.4.2.3 Board meetings

Frequent board meetings are important as a CG mechanism, because they enable the

directors to control the company effectively. Thus, it can be argued that active boards –

those with more frequent meetings - are more likely to monitor financial reporting. On

the other hand, a positive association between board meetings and KPI reporting is

expected in accordance with signalling theory. Active boards’ members will tend to

signal their performance to potential employers. There is limited literature on the

association between frequent board meetings and corporate disclosure in the UK.


99

Laksamana (2008) reported a positive association between board meetings and the

transparency of compensation disclosure. In turn, Nelson et al. (2010) found no

association between board meetings and the amount of executive stock options made by

Australian companies.

The current study aims to provide evidence with regard to the influence of effective boards

using data from UK firms. The following hypothesis is formulated:

H5. A significant association exists between board meetings and KPI reporting

quantity\ quality.

3.4.2.4 Role duality

Role duality occurs if the chief executive officer (CEO) holds the chairman position at

the same time. According to agency theory, effective control over management

performance will only exist if the two roles are separated (Jensen and Meckling, 1976;

Haniffa and Cooke, 2002). Concentration of decision-making power resulting from role

duality could result in opportunistic behaviour on the part of the CEO (Wang and

Hussainey, 2013). Moreover, this may impair the board's governance role regarding

disclosure policies (Li et al., 2008).

It is important to investigate the impact of role duality on KPI reporting because the

results of previous studies that examined the relationship between role duality and

corporate disclosure are mixed. For instance, some studies found an insignificant

influence in terms of duality on CVD (e.g. Ho and Wong, 2001; Cheng and Courtenay,

2006; Ghazali and Weetman, 2006). Other studies reported a significant and negative

relationship between role duality and CVD (Forker, 1992; Haniffa and Cooke, 2002;

Abdelsalam and Street, 2007; Wang and Hussainey, 2013). Thus, the following

hypothesis is formulated:


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H6. A significant association exists between role duality and KPI reporting quantity\

quality.

3.4.3 Audit committee characteristics

This group of variables is concerning with audit committee characteristics that improve

financial reporting monitoring. These variables include: audit committee (AC) size, AC

meetings.

3.4.3.1 AC size

The UK is the first country within the EU to show that the majority of its listed

companies are keen to form ACs (Collier and Gregory, 1999). The Cadbury Committee

(1992) in the UK stated that an audit committee adds assurance to shareholders that

external auditors are serving as guards of their interests. The Smith Report (2003),

states that the AC should monitor the integrity of the financial statements and review

the company’s internal financial control system, as well as its risk management

systems. Moreover, the Corporate Governance Combined Code (2010) in the UK

recommends that the audit committee should involve at least three, or in the case of

smaller companies, two independent non-executive directors. The AC monitoring role

is not only about the financial reporting process, but also extends to the reporting of

non-financial information (Li et al., 2012). Mangena and Pike (2005) suggested that

larger audit committees lead to more effective monitoring, since they are more likely to

have the essential expertise and views to be able to do that. However, mixed results

have been obtained from previous studies. Mangena and Pike (2005) and Tauringana

and Mangena (2009) found no statistically significant relationship between the level of

disclosure and AC size. Felo et al. (2003) and Li et al. (2012) reported a positive

relationship between AC size and the quality of financial reporting. The present study

will empirically explore the relationship between AC size and the quantity and quality


101

of KPI reporting. The following hypothesis is formulated:

H7. A significant relationship exists between AC size and KPI reporting quantity\

quality.

3.4.3.2 AC meetings

An active AC with a high frequency of meetings will have enough time to discharge its

duties. The FRC (2012) recommends that should be no fewer than three AC meetings

during the year. There are a few studies that have investigated the impact of active ACs.

Collier and Gregory (1999) found that UK ACs are effective in their role of overseeing

the external audit and ensuring audit quality. However, their study did not provide

strong evidence that UK ACs are effective in strengthening firms’ internal controls.

Abbott et al. (2004) documented that the activity of the AC has a significant and

negative relationship with the occurrence of the restatement of the annual report. Other

studies have provided evidence on a positive relationship between AC meetings and

subcategories of financial reporting such as internet reporting (e.g. Kelton and Yang,

2008; Li et al., 2012). The present study extends the empirical evidence on the

association between active ACs and the information disclosed by examining the

relationship between AC meetings and KPI reporting.

Thus, the following hypothesis is formulated:

H8. There is a significant relationship between AC meetings and KPI reporting

quantity\ quality.

3.4.4 Ownership structure

Arguably, concentration of ownership affects disclosure levels. This group of variables

include: managerial ownership and major shareholding.


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3.4.4.1 Managerial ownership

According to agency theory, firms with a higher level of managerial ownership would

align the interests of managers and shareholders, and hence may have lower agency

costs (Jensen and Meckling, 1976). Hence, a positive association is expected between

managerial ownership and CVD. However, the findings of previous research into this

relationship are mixed. Several studies showed a positive monitoring influence of

managerial ownership and hence a positive relationship impact upon corporate

disclosure (e.g. Chau and Gray, 2002; Jaing and Habib, 2009). In contrast, Eng and

Mak (2003) reported a negative relationship between managerial ownership and the

quality of corporate disclosures. Wang and Hussainey (2013) argued that managers can

maximize their private benefits by reducing the level of voluntary disclosure based on

management entrenchment theory. On the other hand, Forker (1992) found that the

association between managerial ownership and the quality of share option disclosure is

not statistically significant.

Based upon agency and stakeholder theories, it is anticipated that managers with a high

interest in the company’s shares will be motivated to extend the level of quantity and

quality of KPI disclosed. This behaviour would be explained by the manager’s aim to

reduce the agency problem (agency theory) and to meet other stakeholders’ needs for

information (stakeholder theory). Thus, the following hypothesis is formulated:

H9. There is a significant relationship between managerial ownership and KPI

reporting quantity\ quality.

3.4.4.2 Major Shareholding

The presence of block holders reduces agency costs as it affects the monitoring of

management’s performance (Jensen and Meckling, 1976). A positive association


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between block holder’s ownership and voluntary disclosure is expected according to

agency theory, as managers need to reduce information asymmetry. El-Gazzar (1998)

argued that a high concentration of institutional ownership in a company motivates the

managers to publish more voluntary disclosures in order to maintain confidence in the

company. On the other hand, there is the counterargument that companies with a

concentrated ownership structure do not have to disseminate more information, because

the main shareholders can easily obtain it. They usually have access to such

information perhaps through meetings with company management (Holland, 1998).

In fact, the evidence is mixed on the relationship between institutional ownership and

disclosure in previous studies. While, Eng and Mak (2003) and Wang and Hussainey

(2013) found no significant relationship between institutional investors and voluntary

disclosure, some studies have provided evidence of a negative association between

institutional ownership concentration and disclosure levels in interim reports

(Schadewitz and Blevins, 1998). Other studies (e.g. Mangena and Pike, 2005; Lakhal,

2005) reported a positive association between the two variables. Accordingly, the

current study is motivated by these mixed results to examine the association between

KPI disclosure and institutional investors. Therefore, the following hypothesis is

formulated:

H10. There is a significant relationship between major shareholding and KPI reporting

quantity\ quality.

3.4.5 Capital need variables

Capital need theory assumes a relationship between corporate disclosure and the need

to get funds or to raise capital as cheaply as possible (Choi, 1973). Thus, a plan to raise

capital may represent a good incentive for controlling the quantity and quality of KPIs

reported in the annual report. Corporate managers who look for funds could use one or


104

more of the following resources: the issuance of shares, bonds, or raising loans.

Theoretically, the information asymmetry problem could exist between company

managers and outsider investors who do not have access to internal information about

the company. Therefore, managers should disclose more information in order to obtain

access to more and different resources. High levels of corporate disclosure may result

in a better understanding of the company’s strategies. Thus, managers would be keen to

mitigate information asymmetry through voluntary disclosure (Cooke, 1993; Healy and

Palepu, 2001). Consequently, this will reduce perceived uncertainties and encourage

investors to accept a lower rate of return (Choi, 1973). In line with this argument,

Dhaliwal et al. (2011) found that firms that report non-financial social responsibility

information are more likely to be able to raise larger amounts of equity capital in the

two years following the reporting, compared with non-reporting firms.

From a signalling perspective, managers seeking finance may wish to send good signals

to the investors and debt holders. For investors, such communication is credible

because managers making fraudulent signals will be penalised (Hughes, 1986). On the

other hand, sending good signals to debtors may enable the company to avoid any

additional restrictive covenants in debt contracts. On the other hand, managers could

issue bad news via KPI reporting so as to avoid the consequences of having a reputation

of withholding bad news (Skinner, 1994).

In general, it is expected that directors’ plans to obtain finance would affect KPI

reporting. Managers could use reporting on financial and non-financial KPIs in order

to provide investors with a complete picture regarding corporate strategy and

performance. That would help to raise capital as cheaply as possible, either from local

or international markets.

Moreover, the study extends the literature by examining whether or not managers’


105

plans to access a different source of funds would have a different influence on KPI

reporting quantity and quality. Thus, the following hypotheses are formulated:

H11. There is a significant relationship between issuance of equity and KPI reporting

quantity\ quality.

H12. There is a significant relationship between issuance of bonds and KPI reporting

quantity\ quality.

H13. There is a significant relationship between acquiring loans and KPI reporting

quantity\ quality.

Table 18 summarises the expected signs between KPI reporting quantity \ quality and

the various explanatory factors to be used in this study, based on the findings of the

previous literature.

3.4.6 Firm characteristics as control variables

It is worth noting that the study has to control for firm characteristics that have been

suggested in previous research as determinants of corporate disclosure (e.g. Cooke,

1989; Malone et al., 1993; Wallace et al., 1994; Ahmed and Courtis, 1999; Watson et

al., 2002, Mangena and Pike, 2005; Hussainey and Al-Najjar, 2011). Control variables

include: firm size, profitability, liquidity, leverage, dividend yield, cross listing and

industry.

Firm size is the most common variable that is used in exploring corporate disclosure

determinants. Large firms have more incentives to increase their voluntary disclosure

levels. The size effect can be explained by agency theory (Watts and Zimmerman,

1983, Inchausti, 1997), signalling theory (Wang and Hussainey, 2013), and political

cost theory. In general, the majority of previous disclosure studies have found a positive


106

relationship between a firm’s size and its level of disclosure (e.g. Hossain et al., 1995;

Mangena and Pike, 2005).

Ahmed and Courtis (1999) showed that previous study results have provided mixed

evidence on the association between a firm’s profitability and the level of corporate

disclosure. According to signalling theory, a positive association between disclosure

and profitability is expected. Managers of highly profitable companies tend to signal

their quality to interested parties. Hence, they also could get better rewards and

compensation arrangements (Singhvi and Desai, 1971; Wallace et al., 1994). Moreover,

to avoid external regulations, high profit firms will be motivated to provide more KPIs

that are related to corporate social responsibility.

There are a few studies that have provided mixed findings regarding the relationship

between liquidity and corporate disclosure. The relationship between liquidity and

reporting practices can be explained by agency theory and signalling theory. However,

Watson et al. (2002) claimed that these theories provide mixed predictions in terms of

this relationship. Companies with weak liquidity may increase their disclosure in order

to reduce agency costs and reassure shareholders (Wallace et al., 1994). On the other

hand, according to signalling theory, company managers will have an incentive to

disclose more information if their liquidity is high, to showcase their skills in managing

liquidity risks compared with other managers in companies with lower liquidity ratios.

Furthermore, many empirical studies have denoted leverage (gearing) to be an

important factor that may affect disclosure practices (e.g. Ho and Wong, 2001; Oyelere

et al., 2003; Abraham and Cox, 2007; Hussainey and Al-Najjar, 2011). Based on

agency theory, monitoring costs are higher in highly leveraged firms. To reduce these

costs, they have to disclose more information in order to show their ability to meet any

obligations for the sake of creditors (Jensen and Meckling, 1976). However, empirical


107

evidence on the association between gearing and disclosure is not conclusive.

Additionally, several empirical studies have considered dividend propensity as one of

the key determinants of corporate disclosure (e.g. Archambault and Archambault, 2003;

Hussainey and Al-Najjar, 2011; Wang and Hussainey, 2013). Signalling theory can be

used to explain the impact of dividend propensity on corporate disclosure in the annual

report. Companies with a high tendency to pay more dividends may have fewer

incentives to disclose more information (Naser et al., 2006).

Previous literature has suggested that listing in foreign stock exchanges has a positive

association with corporate disclosure levels (Wallace et al., 1994; Gray et al., 1995;

Mangena and Pike, 2005; Aly et al., 2010). Cross listing, or listing in a foreign market,

gives firms many chances to have access to several alternative sources of finance. The

impact of cross listing can be explained by capital need theory. Participation in

international capital markets offers the opportunity to increase the liquidity of a firm’s

shares (Hope, 2003). Firms with a foreign listing have an incentive to make additional

disclosures to reduce investors’ uncertainty about the performance of the firm (Gray et

al., 1995).

Finally, previous disclosure studies have investigated the relationship between the level

of corporate disclosure and sector type (e.g. Cooke, 1989; Wallace et al., 1994). The

relationship between type of business and reporting practices can be explained by

signalling theory and political cost theory. Signalling theory suggests that the more

homogeneous the industry, the more likely it is that firms will adopt similar reporting

practices (Malone et al., 1993; Wallace et al., 1994; Aly et al., 2010). If a company

within an industry fails to follow the same disclosure practices as others in the same

industry, then it may be interpreted as a signal that it is hiding bad news (Craven and


108

Marston, 1999; Oyelere et al., 2003).

On the other hand, within the framework of political cost theory, different industries are

subject to different political costs (Ball and Foster, 1982). Thus, companies with

vulnerable activities will employ voluntary disclosure to alleviate the political costs

related to their activities (Oyelere et al., 2003).

Some studies found an insignificant relationship between the two variables such as

Wallace et al. (1994). However, the majority of the previous studies found a significant

relationship between sector type and corporate disclosure (Cooke 1992; Craven and

Marston, 1999; Mangena and Pike, 2005; Beretta and Bozzolan, 2004). It is predicted

that KPI reporting would be affected by the type of businesses. Different industries

would be influenced by different and unique value creation activities. The findings

discussed in the previous chapter show that there is a variation between industries in

terms of the quantity and quality of KPI reporting. For instance, Utilities sector

companies have disclosed the largest amount of KPI. This might be explained - in line

with stakeholder theory - by their aim to meet the different needs of their stakeholders

(e.g. creditors, customers, and suppliers). In turn, Oil & Gas firms have provided the

lowest level of KPI reporting quality. Apparently, these companies have avoided the

negative consequences of high quality KPI disclosure. Hence, they control their

disclosures to alleviate the political costs related to their activities. Therefore, the type

of industry should be considered when analysing the determinants of KPI disclosure.

Table 18 summarises the expected signs between KPI reporting and the various control

variables to be used in this study, based on the findings of the previous literature.


109

Table 18 Explanatory variables and their expected relationship with KPI

disclosure based on previous studies

Variables Expected

sign

Examples for previous studies

Directors’

compensations

+ Aboody and Kasznic (2000); Nagar et al. (2003);

Grey et al.(2012)

Managerial

ownership

+ Forker (1992) ; Chau and Gray (2002); Jaing and Habib (2009); Wang and Hussainey( 2013)

Board size + Singh et al. (2004); Lakhal (2005); Cheng and Courtenay (2006); Abdel-Fattah et al. (2007);

Laksamana (2008); Wang and Hussainey( 2013)

Board

composition

+ Forker (1992); Ho and Wong (2001); Haniffa and Cooke (2002); Ajinkya et al. (2005); Tauringana and

Mangena (2009); Hussainey and Al-Najjar (2011)

Board meetings + Laksamana (2008)

Role duality - Forker (1992); Haniffa and Cooke (2002); Ho and Wong (2001); Cheng and Courtenay (2006); Ghazali

and Weetman (2006); Abdelsalam and Street (2007); Wang and Hussainey (2013)

AC size + Felo et al. (2003); Mangena and Pike (2005);

Tauringana and Mangena ( 2009); Li et al. (2012)

AC meetings + Kelton and Yang (2008); Li et al. ( 2012)

Major

shareholding

+ Schadewitz and Blevins (1998); Eng and Mak (2003); Mangena and Pike (2005); Lakhal (2005); Wang and Hussainey( 2013)

The issuance of

shares, bonds

and loans

+ Lang and Lundholm (1993); Boubaker et al. (2011);

Dhaliwal et al. (2011)

Firm size + Hossain et al. (1995); Watson et al. (2002); Boesso

and Kumar (2007); Tauringana and Mangena; (2009); Wang and Hussainey (2013).

Profitability +/- Wallace et al. (1994); Ahmed and Courtis (1999);

Tauringana and Mangena (2009); Hussainey and Al-Najjar (2011).

Leverage +/- Malone et al. (1993); Ahmed and Courtis (1999); Tauringana and Mangena, (2009); Hussainey and Al-

Najjar (2011); Boubaker et al. (2011).

Liquidity +/- Wallace et al. (1994); Watson et al. (2002); Mangena and Pike, (2005); Anis et al. (2012).


110

Cross listing + Cooke (1992); Wallace et al. (1994); Gray et al. (1995); Mangena and Pike (2005); Aly et al. (2010); Elzahar and Hussainey, 2012).

Dividends + Naser et al. (2006); Wang, and Hussainey (2012);

Hussainey and Al-Najjar (2011).

Industry +/- Cooke (1992); Craven and Marston (1999); Mangena and Pike (2005); Beretta and Bozzolan (2004);

Boesso and Kumar (2007); Boubaker et al. (2011); Elzahar and Hussainey (2012).

3.5 The data, descriptive statistics, and the models

To investigate the relationship between KPI reporting and all explanatory variables,

panel regressions are conducted based upon regression models. This section begins

with identifying the sample and the variables used in the present study. Descriptive

statistics for the variables of this study are presented in section 3.5.2. Then, section

3.5.3 will refer to some econometric concerns before carrying out the analyses. Finally,

section 3.5.4 introduces the regression models.

3.5.1 The data

As mentioned earlier, the present study focuses on the annual reports of a sample of

FTSE 350 non-financial UK firms over a five year period (2006-2010). The study

sample is identified as 103 firms with 515 annual reports published between 2006 and

2010. The reports are collected from the companies’ homepages and the Thomson One

Banker database. Firms’ financial characteristics are downloaded from Datastream.

Directors’ compensation data is collected from BoardEx. CG data is manually collected

from the annual reports.


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The steps followed in order to identify the sample firms are indicated in the previous

chapter.21 Subsequently, various observations are excluded for the reasons illustrated in

Panel (A) in Table 19, to end up with 498 firms as the final sample. Panel (B) in the

same table provides a disaggregation of the sample across industries. Finally,

Table 20 illustrates the definition and measurement for each variable in the present

study.

Table 19 Sample Selection and its disaggregation across industries


Starting point: Top 350 UK firms based on market capitalisation, according to the

2011 Financial Times ranking. Financial firms are then excluded. Subsequently, 103 firms are selected randomly following two criteria: 1) each sector is represented in the same proportion as in the starting sample; 2) as firms are arranged according to market

capitalisation; systematic sampling is used by choosing the first company in every sector as a starting point. Then, selection is continued by selecting the third, the fifth

and so on. Then, selection is continued by selecting the third, the fifth and so on. This process results in 515 observations [103 * 5 years (2006, 2007, 2008, 2009, and 2010)]. Thereafter, the following exclusions take place:

n observations

excluded thereafter Reason for exclusion

2 KPI regulation not applicable in 2006 (because of year end date)

4 Missing data on directors’ compensation

6 Missing CG data

5

Missing data on loans, equity, and bonds

issued bonds the year next to the financial statements date.


498 final sample



Basic Materials 40 8.0

Consumer Goods 65 13.1 Consumer Services 107 21.5

Health Care 24 4.8 Industrials 143 28.7 Oil & Gas 54 10.8

Technology 35 7.0 Telecommunications 10 2.0

21

For more details on selecting the sample firms, please see section 2.4.


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Utilities 20 4.0

TOTAL 498 100.0

Table 20 The definition and measurement of the explanatory variables


EXCOMP Executive compensations

The natural logarithm of executives directors’ compensation average

NOEXCOMP Non-executive

compensations

The natural logarithm of non-

executives directors’ compensation average

BORSIZE Board size The total number of directors on board

BORCOMP Board composition The board composition and is

calculated as the number of non-executive directors divided by board

size

BORMEET Board meetings The total number of board meetings during the year

ROLEDUAL Role duality A dummy variable equals 1 if the

chairman is the same person as the CEO of the firm,0 otherwise

ACSIZE Audit committee size The total number of directors in audit committee

ACMEET Audit committee

meetings

The total number of audit committee

meetings during the year

MANGOWN Managerial ownership The percentage of directors’ share interests to ordinary shares

MAJORSHAR Major shareholding The aggregate percentage of shares

that hold by major shareholders (with at least 3% ownership).

FUT_EQUITY The issuance of equity

in t+1

A dummy variable equals 1 if the

firm has issued equity in the next year ,0 otherwise

FUT_BONDS The issuance of bonds

in t+1

A dummy variable equals 1 if the

firm has issued bonds in the next year ,0 otherwise

FUT_LOANS The issuance of loans in t+1

A dummy variable equals 1 if the firm got loans in the next year ,0

otherwise

SIZE Firm size The natural logarithm of market capitalization (WC08001)

PROFITAB Profitability The profitability measured by return

on equity ((WC01651) / ((WC03501))

LIQUDITY Liquidity The ratio of total debt to total capital

(WC08221)

LEVERAGE Leverage The current assets (WC02201) / current liabilities (WC03101)


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DIVYIELD Dividend yield Dividends per share / share price ((WC05376)/(WC08001))

CROSSLIST Cross listing A dummy variable equals 1 if the firm’s shares are traded on foreign

financial markets and 0 otherwise. Note: The definitions and measurement of the dependent variables are presented in Table 5.

3.5.2 Descriptive statistics

Table 21 shows the descriptive statistics for the explanatory variables of the current

study. Panel (A) displays the descriptive statistics for the continuous variables. Whereas

the executive directors’ compensation ranges from £164,960 to £13,000,000, the

average ranges from £24,060 to £315,480 for non-executive directors. The mean of the

directors’ share interests in ordinary shares is 0.05. The median number of directors on

the board is 9 with a minimum of 5 and a maximum of 16. The mean in terms of board

composition illustrates that non-executive directors make up 62% of the board. This

indicates that non-executive directors dominate the board structure of the sample firms,

which can be considered as an indication of board monitoring in these firms. The

number of board meetings as a proxy of board activity shows that the median number

of meetings is 8 per year. The audit committee size median is 4 directors. A median of

4 meetings is recorded for audit committee meetings during the year. That number of

meetings is greater than three, which is the minimum number of audit committee

meetings recommended by FRC (2012). Finally, the major shareholders hold a mean of

38% stake in the firms represented in the sample, with a minimum 4% and a maximum

of 77%.

With regard to firm characteristics, the natural logarithm of market capitalisation for the

sample firms varies from a minimum of 8.00 (£17,000,000) to a maximum of 11.019

(£130 billion) with standard deviation of 0.688 (£18 billion). This huge variation is

expected, as the sample firms are drawn from the FTSE 350 which includes the largest

UK firms. It shows that firm size should be considered as it might have an effect on


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KPI reporting in practice. However, the large variation may also refer to the existence

of outliers. These firms’ profitability mean as measured by ROE is 0.08 which refers, in

general, to the firms’ ability to generate profits from shareholders’ equity. However, the

value of the ROE ratio should exceed the cost of equity capital, in order to add value to

shareholders. The liquidity ratio median is 1.64 times, which indicates that firms in the

sample do not suffer from financial problems in the short run. It shows that firms are

able to cover their short term liabilities through their current assets. These companies

are not highly leveraged, with a mean debt to total capital ratio of 0.366. However, the

minimum of zero and the maximum of 1.42 for the leverage ratio indicate that these

firms vary to some extent in their reliance on debt to finance their investments. Finally,

the sample firms have a dividend to share price ratio with a median of 2.4%. As

companies display good ability to secure current liabilities, it may be implied that these

firms may prefer to retain profits in order to finance their growth.

Panel (B) shows the descriptive statistics for the categorical variables; it indicates that

most of the firms included in the sample (90.16%) are traded on foreign financial

markets. Similarly, it is noted that the majority of the sample firms (95.79%) make a

distinction between the chairman and the CEO positions. According to agency theory,

this distinction between the two roles mitigates the agency problem. It works against

CEO entrenchment, and supports board monitoring. Moreover, the proportion of firms

that got loans in the year following the financial statements’ date (25.7%) is double that

of the proportion who got funds through issuing equity.

Table 21 Descriptive statistics of explanatory variables

Panel (A) Descriptive statistics of continuous variables


EXCOMP 13,000 164.960 1,700 1,100 2,000 498

NOEXCOMP 315,480 24,060 77,305 66,000 43,465 498

MANGOWN 0.53 0.00 0.05 0.01 0.11 498

BORSIZE 16.00 5.00 9.35 9.00 2.43 498

BORCOMP 0.86 0.33 0.62 0.62 0.12 498

BORMEET 17.00 4.00 8.61 8.00 2.51 498


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ACSIZE 6.00 2.00 3.62 4.00 0.87 498

ACMEET 8.00 1.00 3.99 4.00 1.27 498

MAJORSHAR 0.77 0.04 0.38 0.39 0.17 498

SIZE 11.019 8.00 9.194 9.064 0.688 498

PROFITAB 0.52 -0.17 0.08 0.07 0.09 498

LIQUIDITY 8.57 0.26 1.64 1.33 1.32 498

LEVERAGE 1.42 0.00 0.366 0.338 0.279 498

DIVYIELD 0.219 0.00 0.030 0.024 0.032 498

Panel (B) Descriptive statistics for the categorical variables

Variable Proportion N

FUT_LOANS: Proportion of firms got loans in the year next to the financial statements date. 25.7% 498

FUT_BONDS: Proportion of firms issued bonds in the

year next to the financial statements date. 21.48% 498

FUT_EQUITY: Proportion of firms issued equity in the year next to the financial statements date. 13.25% 498

CROSSLIST: Proportion of firms whom shares are

traded in foreign financial markets 90.16% 498

ROLEDUAL : Proportion of directors who are the chairmen and the CEO for a company at the same time 4.21% 498

Panel A displays descriptive statistics of continuous variables used in the present study as proxies for

firm characteristics and corporate governance attributes; EXCOMP is executives directors’

compensation average (in thousands); NOEXCOMP is non-executives directors’ compensation average;

MANGOWN is the managerial ownership which is computed as a percentage of directors’ share

interests to ordinary shares; BORSIZE is the total number of directors on board; BORCOMP is the

board composition and is calculated as the number of non-executive directors divided by board size;

BORMEET is the total number of board meetings during the year; ACSIZE is the is the total number of

directors in audit committee; ACMEET is the total number of audit committee meetings during the

year; MAJORSHAR is the aggregate percentage of shares hold by major shareholders (with at least 3%

ownership), SIZE is the natural logarithm of market capitalization (in £million); PROFITAB is the

profitability measured by return on equity (the ratio of net income to book value of equity); LIQUDITY

is measured by the current assets to current liabilities ratio; LEVERAGE is calculated as the ratio of

total debt to total capital; DIVYIELD is a proxy for dividend policy (dividends per share / share price).

Table 22 illustrates the descriptive statistics of the dependent variables which have been

used in the main analysis or in further analyses. Panel (A) presents the descriptive

statistics for the KPI quantity scores. The number of KPIs disclosed in the KPI section

(QNTKSEC) by the sample firms ranges from a minimum of zero to a maximum of 24

KPIs. The median of QNTKSEC is 6 KPIs. It appears that the majority of the KPIs

reported are financial KPIs. The median number of financial KPIs disclosed in the KPI

section (QNFKS) is 5 KPIs. While the median of non-financial KPIs disclosed in the

KPI section (QNNFKSEC) is only one KPI, after considering the KPIs disclosed


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outside the KPI section, the median number of non-financial KPIs disclosed in the

whole report (QNNFKREP) is found to be 2 KPIs. It appears that the KPIs disclosed

outside the KPI section are more likely to be one non-financial KPI. This conclusion is

confirmed when comparing the mean of QNTKSEC (7.49) KPIs with the mean of

QNTKREP (8.15) KPIs.

Regarding KPI disclosure quality, the quality level for KPIs disclosed in the KPI

section (QLTKSEC) ranges from 0 to 0.688. For the KPIs disclosed in the KPI section,

the mean (median) of financial KPI reporting quality (QLFKS) is 0.345 (0.375). The

level of non-financial KPI reporting quality (QLNFKSEC) is lower, with a mean

(median) of 0.267 (0.286). As a result, the mean (median) of KPI reporting quality

(QLTKSEC) is 0.363 (0.375). However, it seems that the high quality of KPI reporting

outside the KPI section has driven the total quality of KPI reporting QLTKREP to be

slightly higher than the corresponding QLTKSEC, with a mean (median) of 0.37

(0.388).

Table 22 Descriptive statistics of dependent variables

Panel (A) Descriptive statistics for KPI Quantity scores


QNTKSEC 24.00 0.00 7.49 6.00 5.08 498

QNFKS 19.00 0.00 5.34 5.00 3.50 498

QNNFKSEC 15.00 0.00 2.17 1.00 2.92 498

QNNFKREP 16.00 0.00 2.84 2.00 3.38 498

QNTKREP 24.00 0.00 8.15 7.00 5.38 498

Panel (B) Descriptive statistics for KPI Quality scores


QLTKSEC 0.688 0.000 0.362 0.375 0.174 498

QLFKS 0.691 0.000 0.345 0.375 0.175 498

QLNFKSEC 0.786 0.000 0.267 0.286 0.251 498

QLNFKREP 0.818 0.000 0.309 0.327 0.254 498

QLTKREP 0.665 0.000 0.370 0.388 0.170 498

Panel (A) displays descriptive statistics for KPI quantity scores: QNFKS is financial KPIs disclosed in

the KPI section; QNNFKSEC is non-financial KPIs disclosed in the KPI section; QNNFKREP Non-

financial KPIs disclosed in the whole report; QNTKSEC is the total number of financial and non-

financial KPIs disclosed in the KPI section. QNTKREP is the total number of financial and non-

financial KPIs disclosed in the whole report.

Panel (B) displays descriptive statistics for KPI quality scores: QLFKS is the quality of financial KPIs


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disclosed in the KPI section; QLNFKSEC is the quality of non-financial KPIs disclosed in the KPI

section; QLNFKREP the quality of non-financial KPIs disclosed in the whole report; QLTKSEC is the

aggregated quality of financial and non-financial KPIs disclosed in the KPI section. QLTKREP is the

aggregated quality of financial and non-financial KPIs disclosed in the whole report.

3.5.3 Econometric procedures

Cooke (1998) states that the transformation of data is basically helpful in many cases:

when non-linear relationship exists between dependent and independent variables, in

the event that the errors are not nearly a normal distribution, where a problem of

hetroscedasticity exists, or when the relationship between dependent and independent

variables is monotonic. Based upon the original distribution of the scores, common

transformations include logarithm, square root, inverse, reflect and log, reflect and

square root, reflect and inverse (Tabachnick and Fidell, 2007, p.87). Following most of

the disclosure studies (e.g. Li et al., 2012); many continuous variables have been

transformed. Directors’ compensation (EXCOMP, NOEXCOMP) and firm size (SIZE)

are transformed using the log of the original values in order to become more

approximate to a normal distribution (Cooke, 1998; Pallant, 2005; Tabachnick and

Fidell, 2007).

Furthermore, many procedures are performed to avoid multicollinearity among the

independent variables. A perfect relationship between these variables would affect the

reliability of the estimates, and might cause a wide degree of inflation with regard to

the standard errors for the coefficient (Acock, 2008). Tabachnick and Fidell (2007)

state that multicollinearity among independent variables results in a problem in terms of

assessing the importance of each dependent variable in the regression. Therefore, it is

needed to compare the total relationship of the independent variables with the

dependent variable (correlation) and the correlations of the independent variables with

each other (in the correlation matrix) (Tabachnick and Fidell, 2007). The Pearson

correlation matrix is the initial tool to detect multicollinearity. Gujarati (2003) indicates


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that collinearity among the independent variables is acceptable if the correlation

coefficient (r) is a maximum of 0.80.

At an earlier step in the analysis, any proxy that is found to have a strong relationship

with another explanatory variable is replaced with another one. For instance, the

number of non-executives on the board was introduced as a proxy for board

independence. However, it is replaced with another proxy which is the number of non-

executive directors divided by the board size. Moreover, the Pearson correlation matrix

is illustrated in Table 23 and indicates that multicollinearity is not a problem in the

present study. It is clear that all associations among the explanatory variables are below

0.80.22

Finally, Table 23 shows that the correlation is positive and statistically significant

between proxies that are used to separately measure KPI reporting quantity or quality.

This shows a consistency among each group of measures in capturing the required

information. In addition, a positive and statistically significant relationship is found

between the number of KPIs disclosed in the KPI section and their quality (ρ = 0.76). It

is shown that the quantity and the quality of KPI reporting share the same determinants.

Each of KPI reporting quantity and quality is positively correlated with most of the CG

attributes and firm characteristics (i.e. Executive compensation; Non-executive

compensation; Board size; Board composition; Audit committee size; Audit committee

meetings; Firm size). In contrast, KPI reporting quantity\quality looks to be negatively

associated with role duality, liquidity, managerial ownership, and major shareholding.

However, further investigation is needed to obtain empirical evidence on the KPI

reporting determinants. This investigation should take into account the mutual effects

of these determinants.

22

Further check for multicollinearity is performed by calculating the variance inflation factor (VIF) after

carrying out each regression.


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3.5.4 Regression models

As mentioned above, a positive relationship exists between the quantity of (financial\

non-financial\total) KPIs disclosed in annual reports and their quality. This is

considered as a motivation for examining whether or not both KPI reporting quantity

and quality are derived from the same determinants. To empirically test the relationship

between the quantity and quality of KPI reporting and the proposed determinants, the

following models are employed:

QNTKSEC = α + β1 EXCOMP + β2 NOEXCOMP + β3 BORSIZE+ β4 BORCOMP+

β5BORMEET+ β6 ROLEDUAL+ β7 ACSIZE+ β8 ACMEET + β9MANGOWN + β10

MAJORSHAR+ β11 FUT_EQUITY+ β12 FUT_BONDS+ β13 FUT_LOANS+ Firm

characteristics as control variables+ ε.

QLTKSEC= α + β1 EXCOMP + β2 NOEXCOMP + β3 BORSIZE+ β4 BORCOMP+

β5BORMEET+ β6 ROLEDUAL+ β7 ACSIZE+ β8 ACMEET + β9MANGOWN + β10

MAJORSHAR+ β11 FUT_EQUITY+ β12 FUT_BONDS+ β13 FUT_LOANS+ Firm

characteristics as control variables+ ε.

Where:

QNTKSEC = the total number KPIs disclosed in the KPI section. QLTKSEC = the

aggregated quality average for financial and non-financial KPIs disclosed in the KPI

section. α = the intercept.

β1 …….β 13= Regression coefficients.

ε = Error term


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Table 23 Pearson correlation matrix for explanatory variables

*Significance at the 5% level or above. All variables are defined in Table 5 and Table 18.

Variable QNTKSEC QLTKSEC QNFKS QNNFKSEC QNNFKREP QNTKREP QLFKS QLNFKSEC QLNFKREPQLTKREP EXCOMP NOEXCOMP MANGOWN BORSIZE BORCOMP BORMEET ROLEDUAL ACSIZE ACMEET MAJORSHAR FUT_EQUITY FUT_BONDS FUT_LOANS SIZE PROFITAB LIQUIDITY LEVERAGEDIVYIELD CROSSLIST

QNTKSEC 1

QLTKSEC 0.7645* 1

QNFKS 0.8915* 0.7041* 1

QNNFKSEC 0.6945* 0.4302* 0.3399* 1

QNNFKREP 0.6321* 0.4659* 0.3575* 0.7972* 1

QNTKREP 0.9499* 0.7658* 0.8655* 0.6192* 0.7458* 1

QLFKS 0.7328* 0.9190* 0.7840* 0.3270* 0.3820* 0.7410* 1

QLNFKSEC 0.5963* 0.5292* 0.3545* 0.8348* 0.6443* 0.5286* 0.4220* 1

QLNFKREP 0.5697* 0.5494* 0.3835* 0.6840* 0.8160* 0.6407* 0.4531* 0.8354* 1

QLTKREP 0.7291* 0.9614* 0.6740* 0.4040* 0.4944* 0.7701* 0.8838* 0.5052* 0.5959* 1

EXCOMP 0.2656* 0.3233* 0.2721* 0.1254* 0.2357* 0.3127* 0.2942* 0.2013* 0.2937* 0.3380* 1

NOEXCOMP 0.2769* 0.2866* 0.2146* 0.2455* 0.3160* 0.3111* 0.2419* 0.2266* 0.2863* 0.2865* 0.5655* 1

MANGOWN -0.1146* -0.0958* -0.0949* -0.1014* -0.1364* -0.1324* -0.0586 -0.1481* -0.1764* -0.1004* -0.3139* -0.2271* 1

BORSIZE 0.2830* 0.2790* 0.2452* 0.2423* 0.2344* 0.2829* 0.2787* 0.2396* 0.2241* 0.2641* 0.4747* 0.3240* -0.1693* 1

BORCOMP 0.2050* 0.2975* 0.2164* 0.0846 0.1706* 0.2366* 0.2815* 0.1647* 0.2546* 0.3110* 0.5487* 0.2952* -0.1892* 0.1822* 1

BORMEET -0.076 -0.0176 -0.0597 -0.1020* -0.0496 -0.0491 -0.0371 -0.087 -0.0457 0.0073 -0.1055* -0.0827 -0.1177* -0.1387* -0.0098 1

ROLEDUAL -0.1136* -0.1532* -0.0849 -0.0992* -0.1289* -0.1296* -0.1333* -0.1429* -0.1765* -0.1647* -0.0816 -0.1746* 0.2866* -0.0607 -0.0981* -0.0813 1

ACSIZE 0.1188* 0.2542* 0.1225* 0.0604 0.1198* 0.1493* 0.2212* 0.1503* 0.1808* 0.2515* 0.4080* 0.2438* -0.2416* 0.5208* 0.3606* -0.0211 -0.0305 1

ACMEET 0.1641* 0.2763* 0.1742* 0.0621 0.1289* 0.1937* 0.2675* 0.1222* 0.1789* 0.2865* 0.3307* 0.2300* -0.1472* 0.2892* 0.3778* 0.2168* -0.0053 0.3483* 1

MAJORSHAR -0.1669* -0.1323* -0.1496* -0.1343* -0.2634* -0.2348* -0.1248* -0.1201* -0.2167* -0.1508* -0.4077* -0.3377* 0.3728* -0.3992* -0.0559 -0.0647 0.1725* -0.2867* -0.1587* 1

FUT_EQUITY 0.0285 -0.0231 0.0483 0.0041 -0.0373 0.0063 -0.0079 0.0127 -0.0202 -0.0335 -0.0686 -0.008 0.0042 -0.0255 0.0691 0.0341 0.0948* -0.0414 -0.0103 0.0898* 1

FUT_BONDS 0.2335* 0.1585* 0.1975* 0.1899* 0.2125* 0.2452* 0.1410* 0.1492* 0.1677* 0.1575* 0.4223* 0.3044* -0.2190* 0.4455* 0.2712* -0.019 -0.0368 0.3169* 0.2056* -0.3041* -0.0314 1

FUT_LOANS 0.2605* 0.1823* 0.2331* 0.2156* 0.2350* 0.2670* 0.1926* 0.1836* 0.2000* 0.1653* 0.2978* 0.2826* -0.1347* 0.3269* 0.1915* -0.0213 -0.0091 0.1707* 0.1687* -0.1988* 0.1767* 0.3076* 1

SIZE 0.2827* 0.2827* 0.2452* 0.2274* 0.2935* 0.3104* 0.2787* 0.2083* 0.2554* 0.2682* 0.7749* 0.5562* -0.2488* 0.6956* 0.4310* -0.1532* -0.0437 0.4426* 0.3727* -0.4874* -0.0565 0.5256* 0.4084* 1

PROFITAB -0.0408 0.0041 -0.08 -0.0221 -0.0299 -0.0474 -0.0252 -0.0299 -0.0309 0.0004 0.0817 -0.0255 0.0926* -0.0495 0.0679 -0.0671 -0.0119 -0.0596 -0.0597 0.0234 -0.0418 -0.0714 -0.1258* 0.0732 1

LIQUIDITY -0.1067* -0.0943* -0.0911* -0.0930* -0.1410* -0.1335* -0.0974* -0.1017* -0.1430* -0.1157* -0.3464* -0.2102* 0.3276* -0.2082* -0.2525* -0.0776 0.1073* -0.1513* -0.1889* 0.2286* 0.0405 -0.2118* -0.1396* -0.3042* 0.0791 1

LEVERAGE 0.1073* 0.022 0.0424 0.1430* 0.1169* 0.0907* -0.0141 0.0628 0.0596 0.0085 0.1671* 0.2469* -0.2808* 0.0964* 0.1250* 0.1787* -0.0376 0.0417 0.1703* -0.1613* 0.0435 0.2194* 0.0651 0.1475* -0.0659 -0.4140* 1

DIVYIELD 0.1205* 0.0702 0.0769 0.1470* 0.1188* 0.1227* 0.0682 0.1070* 0.0856 0.0958* 0.0403 0.1511* -0.1269* 0.0638 0.0188 0.1437* -0.035 0.047 0.0709 -0.1682* 0.1206* 0.0573 0.0553 -0.0046 0.0436 -0.1740* 0.3175* 1

CROSSLIST 0.1627* 0.1042* 0.1508* 0.1303* 0.0669 0.1195* 0.0895* 0.1171* 0.0578 0.051 0.1429* 0.2106* -0.0672 0.1489* 0.1274* -0.0039 0.0106 0.0718 -0.0722 -0.1093* 0.0496 0.1236* 0.1789* 0.2041* 0.0249 0.005 0.1548* 0.0102 1


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3.6 The main analyses

The main analyses aim at getting clear answers to research questions 2 and 3. By

employing the models illustrated in the previous section, the hypotheses that were

developed in section 3.4 are tested. This section includes the basic procedures to ensure a

high degree of confidence in the results in 3.6.1. Additionally, the empirical results are

provided in 3.6.2.

3.6.1 Econometric procedures

One of the key concerns that could negatively affect the confidence in terms of the

regression results are the outliers. The term ‘outlier’ refers to an observation of the

dependent or independent variables, with values that are inconsistent with the rest of the

observations in the data set (Rawlings et al., 1998, p.331). Severe outliers lead to an

asymmetric distribution of the residuals, and may dominate the results (Acock, 2008).

There are many ways to detect outliers and identify the influential observations. Using

software such as Stata makes it easier to conduct a Shapiro-Wilk test to check whether or

not the residuals are normally distributed around the predicted dependent variable scores.

Rejecting the null hypothesis in that test (normality) gives an indication of the outliers’

existence.

To identify these outliers (Acock, 2008), the software is asked to predict the estimated

score based upon the regression, then to predict the residuals and sort them according to

their values. Consequently, observations with regard to the highest and lowest Z score are

to be considered as outliers.

Tabachnick and Fidell (2007) indicate two strategies to reduce the influence of the

outliers after considering the following alternatives:


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a) At first the original data should be checked to ensure its accuracy.

b) If the outliers are caused by a variable which is highly correlated with

other variables, but is not important in the analysis, it can be eliminated.

c) If the cases with extreme scores are not part of the targeted population,

they should be deleted.

If the outliers are retained in the analysis, there are two possible strategies to reduce their

impact on the analysis:

a) Changing the largest and smallest scores by assigning specified scores to them.

b) Transforming variables to make the distribution more nearly normal.

In order to maintain the sample size, the present study does not adopt any option that

leads to the elimination of some cases. Moreover, all cases are part from the target

population, and hence they represent a source of information that could be critical to the

analysis. The study deals with the outliers by adopting both of these strategies.

Following several studies (e.g. Aggarwal et al., 2009); the data is winsorised by setting

all outliers at 99% of the data. This option is usually preferred by researchers to reduce

the impact of outliers on the estimators (Tabachnick and Fidell, 2007).

As indicated before with regard to some independent variables, transformation is also

utilised with regard to the scores of the dependent variables. Rawlings et al. (1998) state

that transformation could handle heterogeneous variances in dependent variables’ data so

as to make them more homogeneous on the transformed scale. As mentioned above, there

are several methods to transform variables. Cook (1998) argued that the following

methods are used in disclosure studies - ranks, percentile ranks, normal scores, and the

log of odds ratio. He suggests that selecting the relevant method of transformation would


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be affected by the data itself. For instance, getting the square root of scores (SQRT)

would suit a distribution with a moderate positive skewness, while using the log of scores

would be more convenient for a distribution with a significant positive skewness

(Tabachnick and Fidell, 2007). Therefore, Tabachnick and Fidell (2007) conclude that

researchers should try more than one method of transformation in order to have the

fewest outliers, and get the nearest kurtosis and skewness values that are too close to

zero. Accordingly, the study carries out many regressions after undertaking different

methods of transformation (log of dependent and\or independent variables, SQRT of

dependent and\or independent variables, normal scores). The best improvement in the

analysis is achieved by transforming dependent scores using SQRT, as long as

transforming many dependent variables is done by applying log as mentioned above.

Wooldridge (2003) points out that using panel data has some econometric concerns. The

unobserved residuals for the unit (the firm in the current study) mean that these

unobserved residuals may be serially correlated - because of an unobserved effect - across

years.23 Furthermore, the residuals for a specific year may be correlated across firms

(Petersen, 2009). Failure to deal with cross-sectional dependence (the firm effect) and

time series dependence (the time effect) in the panel data, leads to biased standard errors

and incorrect inferences (Petersen, 2009; Gow et al., 2010). Petersen (2009) argues that

even if one source of correlation is addressed (e.g. by including time dummies to correct

for the time effect), yet the other effect (i.e. the firm effect) will be left in the data. Gow

et al. (2010) evaluated the different methods that are applied by researchers in accounting

studies to address firm and time effects. These methods include OLS standard errors,

White standard errors, Newey-West standard errors, Fama-MacBeth, Z2, as well as

23 ‘The key feature of panel data that distinguishes it from a pooled cross section is the fact that the same

cross-sectional units (individuals, firms, or counties) are followed over a given time period’ (Wooldridge,

2003, p.11).


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robust standard errors clustered by time, firm and both. Those methods are examined

under four different settings: a) non-existence of firm and time effects, b) existence of

firm effects only, c) existence of time effects only, and d) existence of both firm and time

effects. In contrast to other methods, they found that only clustering by firm and time

(CL-2) produced well-specified tests statistics under all assumptions. Rather, when

applying other methods, the estimates either are not robust with regard to any form of

effects (in the event of performing OLS and White standard errors), or just robust for one

form of effect (in the event of following other methods). Consistent with this finding,

Petersen (2009) states that fixed and random effect models produce unbiased estimates in

the case of the permanent firm effect. On the other hand, clustering by firm and time

effects is unbiased, and correctly produces confidence intervals whether the firm effect is

permanent or temporary.

The current study uses a panel data on a sample of UK companies for five years.

Therefore, the study employs clustering by both firm and time in order to consider any

unobserved firm and time effects within the panel data set. As indicated above, this

technique would improve the accuracy of the analysis through getting correct estimates

and inferences. To employ clustering by firm and time technique, the necessary

commands and programing instructions are provided by Petersen (2009). The researcher

had to follow these instructions and install the commands before carrying out the

analysis. 24

In order to empirically test the hypotheses of the present study, all regressions are run

using Stata software. Stata is a flexible statistical program that allows users to apply pre-

programmed commands or use further commands that have been invented by different

users (Hamilton, 2006).

24

The needed commands and programing instructions are provided in:

http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/se_programming.htm


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As mentioned before, after carrying out each regression, the variance inflation factor

(VIF) is calculated. This procedure is an additional step to ensure that explanatory

variables are not extremely correlated. The rule that has been applied is that correlation

between independent variables is accepted as long as VIF is still smaller than 10

(Gujarati, 2003; Acock, 2008).

Despite the fact that all procedures showed that multicollinearity among explanatory

variables is acceptable in accordance with previous studies, the study considers any

concerns with regard to hidden correlations that might exist between CG variables.

Therefore, these variables are grouped into groups: executive and non executive directors'

compensation, board characteristics, audit committee characteristics, and ownership

structure. Then, these groups are included separately - together with the control variables

- in different models from model (1) to model (5). Additionally, capital need variables are

included - together with control variables - in model (6). Finally, all explanatory variables

are included in model (7).25

3.6.2 Empirical results

As mentioned above, all regressions are run using Stata software. The analyses include

industry dummies to control for the industry effect. Moreover, the study employs

clustering by firm and time effects to counter any unobserved cross-sectional and time

series dependence. The results regarding the determinants of the quantity of and the

quality of total KPIs disclosed in the KPI section are presented in 3.6.2.1. The results

with respect to the determinants of reporting on KPI subcategories are presented in

3.6.2.2.

25

The researcher is grateful to the external examiner Dr Basil Al-Najjar for this suggestion.


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3.6.2.1 The determinants of KPI reporting

Table 24 and Table 25 present the empirical findings with regard to the factors affecting

the quantity of total KPIs disclosed in the KPI section within the annual report

(QNTKSEC), and the quality of reporting with regard to these KPIs (QLTKSEC)

respectively. In general, the findings show that the regression models are significant with

high F values at the 1% level. This means that the proposed determinants explain a

significant part of the quantity and quality of KPI reporting. In addition, the VIF values

refer to the absence of multicollinearity between the explanatory variables. The adjusted

R2 values in Table 24 and Table 25 indicate that the proposed models can explain 14.8%

- 22.3% of the variation in QNTKSEC, and 11.8% - 24.5% of the total variation in

QLTKSEC.

Table 24 KPI reporting in the KPI section: the determinants of quantity

Variable Mo1 Mo2 Mo3 Mo4 Mo5 Mo6 Mo7

EXCOMP 0.517

0.324

0.369

0.271

NOEXCOMP 0.785**

0.316

0.61**

0.262

BORSIZE 0.09**

0.035

0.12**

0.039

BORCOMP 1.56**

0.606

1.27**

0.603

BORMEET -0.04*

0.022

-0.05*

0.024

ROLEDUAL -0.6**

0.258

-0.57*

0.32

ACSIZE -0.027

0.091

-0.146

0.108

ACMEET 0.086

0.071

0.092

0.078

MAJORSHAR 0.171 0.333

-0.008 0.322

MANGOWN -0.034

0.754

0.447

0.695

FUT_EQUITY 0.048

0.21

0.001

0.207


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FUT_BONDS 0.169*

0.097

0.12**

0.045

FUT_LOANS 0.347**

0.147

0.284*

0.145

SIZE 0.292*

0.173

0.380**

0.127

0.149 0.132

0.47***

0.124

0.54***

0.128

0.40***

0.111

-0.253 0.197

PROFITAB -1.033

0.72

-0.77

0.7

-0.811

0.628

-0.846

0.767

-0.995

0.709

-0.69

0.663

-0.42

0.593

LIQUIDITY 0.079 0.074

0.065 0.072

0.095 0.062

0.07 0.071

0.072 0.073

0.074 0.072

0.087 0.066

LEVERAGE -0.096 0.297

-0.143 0.295

-0.002 0.234

-0.15 0.272

-0.08 0.283

-0.078 0.243

-0.11 0.227

DIVYIELD 2.221

2.525

1.829

2.633

1.826

2.268

2.309

2.663

2.649

2.54

2.226

2.428

0.826

2.007

CROSSLIST 0.281 0.247

0.228 0.263

0.224 0.241

0.335 0.252

0.273 0.245

0.218 0.25

0.218 0.289

Constant -3.7** 1.453

-5.2*** 1.503

-0.743 1.063

-2.5** 1.113

-3.04** 1.206

-1.707* 1.027

-2.188 1.52

F 9.2*** 9.9*** 8.5*** 8.02*** 7.65*** 8.32*** 5.9***

Adj R-squared 0.16 0.162 0.195 0.154 0.148 0.164 0.223

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498

***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level.

Dependent variable: QNTKSEC is the total number of financial and non-financial KPIs disclosed in the

KPI section. Explanatory variables: executives’ compensation in Mo1; non-executives’ compensation

in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure

variables in Mo5; capital need variables in Mo6; and all explanatory variables used in the analyses

in Mo7. All variables are defined in Table 5 and Table 18. All regressions include industry dummies.

Standard errors in the second line for each variable and are corrected for firm and t ime clustering.

Table 25 KPI reporting in the KPI section: the determinants of quality


EXCOMP 0.2**

0.061

0.127**

0.04

NOEXCOMP 0.18***

0.055

0.14***

0.038

BORSIZE 0.020**

0.007

0.023**

0.008

BORCOMP 0.42***

0.112

0.251**

0.107

BORMEET -0.002

0.003

-0.003

0.004


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ROLEDUAL -0.17***

0.038

-0.191**

0.06

ACSIZE 0.022 0.021

0.001 0.026

ACMEET 0.034***

0.01

0.033**

0.011

MAJORSHAR 0.075

0.066

0.065

0.063

MANGOWN -0.045 0.152

0.156 0.109

FUT_EQUITY -0.014 0.045

-0.025 0.042

FUT_BONDS 0.006

0.01

-0.013

0.01

FUT_LOANS 0.045**

0.016

0.027 0.018

SIZE 0.031 0.031

0.07***

0.017

0.017 0.023

0.06***

0.018

0.11***

0.021

0.09***

0.021

-0.077**

0.034

PROFITAB -0.13

0.137

-0.063

0.137

-0.058

0.113

-0.019

0.141

-0.117

0.131

-0.083

0.125

0.029

0.106


0.002 0.014

0.01 0.012

0.003 0.012

0.004 0.014

0.004 0.014

0.008 0.011

LEVERAGE -0.043 0.067

-0.053 0.063

-0.031 0.049

-0.057 0.061

-0.039 0.062

-0.034 0.056

-0.047 0.057

DIVYIELD 0.468

0.646

0.401

0.673

0.353

0.63

0.422

0.685

0.623

0.689

0.55

0.648

0.151

0.604

CROSSLIST 0.032 0.062

0.019 0.065

0.016 0.058

0.053 0.057

0.03 0.061

0.024 0.061

0.032 0.061

Constant -0.7** 0.243

-0.96** 0.311

-0.002 0.161

-0.261 0.18

-0.50** 0.223

-0.283 0.209

-0.71** 0.275

F 9.4*** 9.11*** 10.0*** 9.9*** 6.2*** 5.7*** 7.4

Adj R-squared 0.147 0.137 0.194 0.156 0.118 0.12 0.245

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


Dependent variable: QLTKSEC is the aggregated quality score of financial and non-financial KPIs disclosed

in the KPI section. Explanatory variables: executives’ compensation in Mo1; non-executives’

compensation in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4;

Ownership structure variables in Mo5; capital need variables in Mo6; and all explanatory variables

used in the analyses in Mo7. All variables are defined in Table 5 and Table 18. All regressions include

industry dummies. Standard errors in the second line for each variable and are corrected for firm and time

clustering.


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The findings illustrate the impact of each group of variables on the quantity and quality of

KPI reporting. Regarding the effect of Directors’ compensation, model (1), model (2),

and model (7) in Table 24 indicate that only non-executives’ compensation has a

significant influence on QNTKSEC at a level of 5%. In contrast, model (1) and model (7)

in Table 25 show that executives’ compensation has a significant impact on QLTKSEC at

the 5% level. Moreover, model (2) and model (7) in Table 25 indicate that non-

executives’ compensation have a positive and significant relationship with QLTKSEC at

the 1% level. Therefore, H1 that expects a positive association between executive

compensation and the quantity as well as the quality of KPI reporting, is partially

accepted. On the other hand, H2 is supported, as non-executives’ compensation are found

to have a positive and significant effect on KPI reporting quantity and quality.

Concerning the effect of board characteristics, the results in model (3) and model (7) in

Table 24 and Table 25 show that, board size and board composition have a positive and

significant impact on QNTKSEC as well as on QLTKSEC. Hence, these results support

H3 and H4. In contrast, the reported results in model (3) and model (7) in Table 24

reveal that the board meetings variable has a weak and negative influence on QNTKSEC

at a level of 10%, whereas it has no significant effect on QLTKSEC as is indicated in

model (3) and model (7) in Table 25 . Hence, H4 is partially accepted. Moreover, it is

indicated that role duality has a negative and significant influence on QNTKSEC as well

as on QLTKSEC. These results lead us to accept H6, which anticipates a significant

association between role duality and KPI reporting.

H7 posits that a significant relationship exists between AC size and KPI reporting, but the

results reported in model (4) and model (7) in Table 24 and Table 25 illustrate that this

relationship is not statistically significant. Hence, H7 is not accepted. However, AC

meetings significantly affect QLTKSEC according to the results reported in model (4)


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and model (7) in Table 25, while these meetings have an insignificant association with

QNTKSEC. Therefore, H8, which expects a significant relationship between AC

meetings and KPI reporting, is partially accepted.

With regard to the effect of ownership structure, the results reported in model (5) and

model (7) in Table 24 and Table 25 do not provide supporting evidence for the

relationship between the quantity and quality of KPI reporting on the one hand, and

managerial ownership as well as major shareholding, on the other. As a result, H9 and

H10 cannot be accepted.

Concerning the impact of capital need variables, model (6) and model (7) in Table 24 and

Table 25 indicate that plans to issue equity do not influence KPI reporting. Thus, these

results do not support H11. On the other hand, the plans to issue bonds have a statistically

significant and positive relationship with KPI reporting quantity rather than with its

quality. Therefore, H12, which posits a significant relationship between the plans to issue

bonds and KPI reporting, is partially accepted. Furthermore, model (6) and model (7) in

Table 24 show that the intent to obtain loans has a positive and statistically significant

relationship with KPI reporting quantity at levels of 5% and 10% respectively. However,

the plans to acquire loans appear to have a significant relationship with KPI reporting

quality at a level of 5% in model (5) in Table 25. Thus, H13 can be partially accepted.

Finally, the findings do not support a significant association between the firm

characteristics group (i.e. firm size, profitability, liquidity, leverage, dividend yield, and

cross listing) and QNTKSEC as well as QLTKSEC. The exception to this conclusion is


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firm size, which has a positive and significant effect on KPI reporting in the majority of

the models reported in Table 24 and Table 25 . 26

3.6.2.2 The determinants of reporting on KPI subcategories

This section reports the empirical findings with regard to the determinants of reporting on

financial as well as non-financial KPIs. These findings would help in investigating

whether KPI reporting quantity and quality are derived from the same factors.

3.6.2.2.1 The determinants of financial KPI reporting

Table 59 in Appendix (2) shows the key factors that affect reporting in terms of the

quantity of financial KPIs (QNFKS). High F values indicate that the regression models

are significant at the 1% level. The models proposed can explain 19.5% - 24.0% of the

variation in QNFKS. Table 60 in Appendix (2) presents the determinants of non-financial

KPI reporting (QLFKS). The models proposed can explain 11.1% - 18.0% of the

variation in QNFKS. Table 59 and Table 60 indicate that there are no concerns regarding

multicollinearity between the determinants of QNFKS and QLFKS according to VIF

values.

As illustrated in Table 59, executives’ compensation positively affects QNFKS at a level

of 5% which are executives’ compensation, whereas non-executive compensation has a

weak positive effect on QNFKS at the 10% level (as reported in model 2). With respect to

CG characteristics, board size has a positive and statistically significant association with

QNFKS at the 5% level (as reported in model 3 and model 7). Model (3) in Table 59

suggests that board composition has a positive and significant relationship with QNFKS

at the 5% level (as reported in model 3).

26

To check the robustness of the results, all models reported in Table 24 and Table 25 are re-estimated

using different measurements for some of firm characteristics variables. The findings are almost similar to

the results discussed in 3.6.2.1.


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The findings in Table 59 do not support the significant association between AC

characteristics as well as ownership structure variables with QNFKS. On the other hand,

the results indicate a significant positive relationship between firm’s plans to get loans

and QNFKS at the 5% level (as reported in model 6 and model 7).

Table 60 reveals that the same factors significantly affect QLFKS. In addition, the results

indicate that QLFKS is also positively influenced by AC meetings at a significance level

of 5%, while it has is a negative and significant relationship with role duality.

3.6.2.2.2 The determinants of non-financial KPI reporting

Table 61 and Table 62 in Appendix (2) illustrate the determinants that affect non-

financial KPI reporting quantity (QNNFKSEC) and quality (QLNFKSEC). The

importance of this analysis is that, despite being recommended by the UK CA (2006),

companies do not disclose sufficient KPIs in practice, as displayed in descriptive

statistics. These analyses would introduce the characteristics of companies that pay

attention to enhancing QNNFKSEC and QLNFKSEC.

Table 61 and Table 62 indicate that the proposed explanatory variables explain a

significant part of the variation in QNNFKSEC and QNNFKSEC. The models proposed

can explain 19.5% - 24.0% of the variation in QNNFKSEC, and explain 11.1% -18.8% of

the total variation in QLNFKSEC. As indicated in Table 61 and Table 62, three variables

show identical results with regard to their positive and highly significant association with

QNNFKSEC as well as QLNFKSEC. These variables are: non-executives’ compensation,

board size, and board composition. Moreover, it is noted that QLNFKSEC is influenced

significantly and positively by executives’ compensation (as indicated in model 1 and

model 7 in Table 62), and negatively with both role duality and board meetings (as

indicated in model 2). Finally, the findings provide evidence that incentives to increase


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QNNFKSEC depend on the source for raising capital. Firms show a tendency to increase

the quantity and quality of non-financial KPI reporting when they are planning to obtain

loans at a level of 10% (as noted in model 6 and model 7 in Table 61 for QNNFKSEC,

and model 6 in Table 62 for QLNFKSEC). In contrast, a negative and significant

relationship is documented between getting funds through issuing equity and

QNNFKSEC at a level of 10% (as noted in model 7 in Table 61).

3.7 Discussion of the results

This section discusses the results in detail in terms of the explanatory variables groups.

This discussion links the findings with the hypotheses that are developed in section 3.4.

3.7.1 Directors’ compensation variables

Table 26 summarises the findings with regard to directors’ compensation variables

(executives’ compensation, and non-executives’ compensation).

Table 26 The findings in terms of directors’ compensation variables

Variable QNTKSEC QLTKSEC

Model (1) Model (7) Model (1) Model (7)

EXCOMP (+)** (+)**


NOEXCOMP (+)** (+)** (+)*** (+)***

***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. (+): positive

relationship; (-): Negative relationship. QNTKSEC is the total number of financial and non-financial KPIs

disclosed in the KPI section; QLTKSEC is the aggregated quality score of financial and non-financial

KPIs disclosed in the KPI section. Explanatory variables: EXCOMP is Executive compensation;

NOEXCOMP is Non-executive compensation. Explanatory variables: Mo1 includes executives’

compensation in addition to control variables; Mo2 includes non-executives’ compensation in

addition to control variables; and all explanatory variables used in the analyses are included in Mo7.

The results indicate that executives’ compensation has a positive and statistically

significant relationship with the quality of KPI reporting, but is not significantly

associated with its quantity. These findings are in line with agency and signalling

theories. Shareholders in firms with highly compensated directors would be eager to


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evaluate the results produced by those directors (Jonas, 2007). The analyses’ findings

suggest that highly compensated directors will tend to disclose high quality KPI

information. This information might include their private information about the targets

for each KPI. Consequently, a lower degree of information asymmetry between directors

and shareholders could be achieved. Similarly, it is clear that highly compensated non-

executives are more likely to signal themselves as high quality managers in the

employment market. Accordingly, they improve the level of quantity and quality of KPI

reporting. These results provide evidence suggesting that managerial remuneration plays

an important role for the shareholders. Hence, they support the choice to increase the

quantity and quality of KPI information which is disseminated by the directors. There are

relatively few previous studies that have investigated the link between directors’

compensation and corporate disclosure (e.g. Aboody and Kasznic, 2000; Nagar et al.,

2003; Grey et al., 2012). The findings of the current study add to the literature by

providing evidence on the association between directors’ compensation and both the

amount and quality of disclosure.

3.7.2 Board characteristics

Table 27 presents a summary of the results with regard to board-related variables (board

size, board composition, board meetings and role duality).

Table 27 The findings of board characteristics variables

Variables QNTKSEC QLTKSEC


BORSIZE (+)** (+)** (+)** (+)**

BORCOMP (+)** (+)** (+)*** (+)**

BORMEET (-)* (-)*

ROLEDUAL (-)** (-)* (-)*** (-)** ***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. (+): positive

relationship; (-): Negative relationship. QNTKSEC: the total number of financial and non-financial KPIs

disclosed in the KPI section; QLTKSEC: the aggregated quality score of financial and non-financial KPIs

disclosed in the KPI section. Explanatory variables: BORSIZE: Board size; BORCOMP: Board

composition; BORMEET: Board meetings; ROLEDUAL: Role duality. Explanatory variables: Mo3

includes board characteristics in addition to control variables; and all explanatory variables used in


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the analyses are included in Mo7.

The findings reported indicate that board size variable has a positive and statistically

significant relationship with the quantity and quality of KPI reporting. These findings are

in line with some of the previous literature that examines the relationship between board

size and corporate disclosure (e.g. Laksamana, 2008; Hussainey and Al-Najjar, 2011).

The positive association demonstrates the effectiveness in terms of performing the

monitoring role on the part of UK boards.

These findings might be against Guest’s (2008) argument that UK soft regulations lead to

weakness in performing monitoring role on the part of UK boards. The results suggest

that larger boards increase the quantity of KPI reporting, and improve its quality thanks to

the diversity of expertise on the board. The results could be explained by signalling

incentives. Larger boards have a greater motivation to disclose more high quality KPIs in

order to magnify their performance, and hence more likely to be followed and assessed

by different employers.

The results also indicate that board composition has a positive and significant relationship

with the dependent variables. Although the findings concerning KPI quantity is different

from the findings of Tauringana and Mangena (2009) who reported a negative association

between the proportion of NEDs and the amount of KPI disclosed, that study focused

only on one sector (i.e. the media sector). The findings of the current study show

consistency with the findings of many previous studies that focus on disclosure quantity

(e.g. Li et al., 2008; Laksamana, 2008; Wang and Hussainey, 2013), or disclosure quality

(e.g. Forker, 1992).


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The results support the evidence that boards with a high proportion of non-executive

directors are expected to be more effective in performing the monitoring role, and hence,

affect corporate disclosure positively. The findings suggest that independent directors

might affect KPI reporting positively in order to signal their competence to potential

employers (Fama and Jensen, 1983).

With regard to the board meetings variable, the findings display a weak and negative

relationship between board meetings and the number of KPIs disclosed in the KPI

section. The findings also indicate that board meetings have no significant effect on KPI

reporting quality.

Arguably, active boards with frequent meetings lead to better monitoring in terms of

financial reporting (Laksamana, 2008). However, the findings may be because board

meetings are used for discussing general objectives and polices. Consequently, detailed

issues may be left to the meetings of the relevant sub-committees (the audit committee in

our case). Moreover, it could be claimed that board meetings do not measure accurately

the extent of board activity and its reflection on financial reporting. In this regard, future

research may look for another proxy for board activity. For instance, board meeting

minutes which identify how much of the meeting is allocated to the discussion of

financial reporting issues. This would be a relevant proxy that measures - in detail - the

efficiency of board meetings in terms of discussing disclosure choices.

Concerning the impact of role duality on KPI reporting, the findings indicate that the

relationship is negative and statistically significant between role duality and KPI

reporting quantity as well as its quality. It is observed that the negative impact of role

duality is stronger on the quality of KPI reported in the KPI section. These findings are

consistent with other studies which report a significant and negative relationship between


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role duality and CVD quantity (e.g. Haniffa and Cooke, 2002; Abdelsalam and Street,

2007; Wang and Hussainey, 2013), and quality (Forker, 1992).

The present findings can be interpreted using the propositions of agency theory. The

concentration of decision-making power resulting from role duality could weaken a

board’s monitoring role with regard to disclosure practices (Li et al., 2008). Combining

the two roles of the CEO and the chairman creates resistance against some other CG

forces like NEDs and ACs (Forker, 1992). As a result, a dominant personality leads to a

negative effect on providing more KPI information or improving its quality.

3.7.3 Audit committee characteristics

The present study investigates the relationship between two of the AC characteristics and

KPI reporting (i.e. AC size and AC activity).

Table 28 presents a summary of the findings with regard to these variables.

Regarding AC size, no evidence is found of a relationship between AC size and KPI

reporting quantity or quality. These results are consistent with the finding of previous

studies (i.e. Mangena and Pike, 2005; Tauringana and Mangena, 2009; Elzahar and

Hussainey, 2012) that reported the same findings with respect to AC size association with

disclosure quantity.

Table 28 The findings with regard to AC characteristics



ACSIZE

ACMEET (+)*** (+)**

***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. (+): positive



disclosed in the KPI section. Explanatory variables: BORSIZE: Board size; BORCOMP: Board

composition; BORMEET: Board meetings; ROLEDUAL: Role duality; ACSIZE: Audit committee size;

ACMEET: Audit committee meetings. Explanatory variables: Mo4 includes audit committee


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characteristics in addition to control variables; and all explanatory variables used in the analyses are

included in Mo7.

These results can be interpreted in line with the Corporate Governance Combined Code

(2010) which recommends that the audit committee should involve at least three - or in

the case of smaller companies, two - NEDs. As presented in the descriptive statistics in

Table 21, it seems that UK companies comply with this recommendation. The number

reported for AC members is between 2 to a maximum of 6, and the average as well as the

median number of AC members is almost 4 members. This might causes the insignificant

relationship that was found between AC size and KPI reporting in the present study.

Concerning the impact of active ACs, despite being unable to support a positive

association between AC activity and KPI reporting quantity, the findings show that the

coefficient estimate with regard to ACMEET is positive and highly significant for KPI

reporting quality.

That result indicates the role of AC meetings as a CG mechanism, suggesting that active

ACs would be able to have an oversight of, and control, KPI reporting quality. The

finding supports the argument that frequent AC meetings enable the committee to have

enough time to discharge its duties. As the positive effect on KPI reporting quality has

been achieved with an average of four AC meetings in the current sample, it can be

suggested that UK companies should be encouraged to follow the FRC (2012)

recommendation that asks for many AC meetings during the year, with a minimum of

three.

3.7.4 Ownership structure

The results with regard to ownership structure variables (managerial ownership and major

shareholding) indicate that there is no significant impact of either of them on the quantity


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or the quality of KPI reporting.

The insignificant effect of managerial ownership adds to the mixed results reported in

previous studies (e.g. Chau and Gray, 2002; Eng and Mak, 2003). However, this result is

consistent with Forker (1992) who found that the association between managerial

ownership and quality of share option disclosure is not statistically significant. The result

may be the outcome of two different managerial incentives with contradictory impacts.

The first is the managers’ motivation to increase KPI reporting in order to reduce the

agency problem (based on agency theory). The second is their motivation to achieve self

benefits by withholding some KPIs or reducing the quality of KPI information disclosed.

On the other hand, despite the fact that a high concentration of ownership is considered as

a CG mechanism that motivates managers to reduce agency costs and increase CVD

(Jensen and Meckling, 1976; El-Gazzar, 1998), the findings are not consistent with

agency theory. The results might be due to the fact that the main shareholders have easy

access to KPI information via other means (e.g. interviews with board members).

However, the findings are in line with some previous studies such as those of Eng and

Mak (2003) and Wang and Hussainey (2013) who found no significant relationship

between institutional investors and voluntary disclosure.

3.7.5 Capital need variables

Table 29 presents a summary of the results with regard to capital need variables (the

issuance of equity in t+1, the issuance of bonds in t+1, and the issuance of loans in t+1).

The findings show that managers’ plans to obtain finance from different sources have

different implications for KPI reporting.


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Table 29 The findings of capital need variables



FUT_EQUITY

FUT_ BONDS (+)* (+)**

FUT_LOANS (+)** (+)* (+)** ***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. (+): positive



disclosed in the KPI section. Explanatory variables: MAJORSHAR: Major shareholding; FUT_EQUITY:

The issuance of equity in t+1; FUT_ BONDS: The issuance of bonds in t+1; FUT_LOANS: The issuance

of loans in t+1. Explanatory variables: Mo6 includes capital need variables in addition to control

variables; and all explanatory variables used in the analyses are included in Mo7.

The results do not provide evidence of the relationship between KPI reporting and

managers’ plans to issue new equity. These results could be interpreted as being in line

with Lang and Lundholm’s (2000) study which found that firms that substantially

increase their disclosure levels before the offering may experience a dramatic fall in the

stock price after announcing the offering (which exceeds the increase in the price that

existed before the announcement). In contrast, firms with a consistent level of disclosure

before an announcement might not suffer from this substantial correction reaction in

price. In the current study, it can be argued that there is a trade-off between managers’

incentives to mitigate information asymmetry through increasing KPI disclosure levels

and quality, and their motivation to maintain a steady level of disclosure so that they can

avoid any major decline in share price after an announcement date. This trade-off could

result in the insignificant relationship that exists between the plans to issue equity and

KPI reporting.

With respect to other variables in this group, the findings document a positive and

statistically significant association between the corporate tendency to issue bonds and the

number of KPIs reported in the KPI section. Furthermore, the results show that

companies which plan to obtain loans are more likely to increase the number of KPIs

reported.


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These results confirm the fact that managers are driven by the need for finance to increase

the volume of KPI disclosures. However, managers do not place the same emphasis on

KPI reporting quality. The results provide insight into the impact of the potential source

of finance on disclosure practices. The managers show their focus by sending particular

signals to a sector of the stakeholders (i.e. potential creditors) through publishing more

KPIs, while they do not place the same stress when they communicate with other

investors (i.e. when they plan to issue new equity).

3.7.6 Firm characteristics (control variables)

Six firm characteristics variables are introduced in the current study as control variables

(firm size, profitability, liquidity, leverage, dividend yield, and cross listing).

Interestingly, only firm size shows a significant and positive influence on KPI reporting

quantity and quality. However, similar results have been reported in previous studies. For

instance, it is documented that an insignificant association exists between corporate

disclosure and profitability (e.g. Mangena and Pike, 2005), liquidity (e.g. Mangena and

Pike, 2005; Anis et al., 2012), leverage (e.g. Ho and Wong, 2001; Oyelere et al., 2003;

Abraham and Cox, 2007), dividend yield (Naser et al., 2006), and cross listing (e.g.

Oyelere et al., 2003; Elzahar and Hussainey, 2012).

Notably, firm size shows a negative association with KPI reporting quality in model (7)

of Table 25. This result is in contrast with the majority of previous studies which found a

positive association between firm size and disclosure quantity or quality for CVD (e.g.

Hossain et al., 1995; Mangena and Pike, 2005; Hassan et al., 2006; Boesso and Kumar,

2007; Tauringana and Mangena, 2009; Wang and Hussainey, 2013). Model (7) of Table

25 includes the whole set of variables used in the current study. Giving that the negative

sign of firm size coefficient is reported only in this model, while it shows a positive

direction in other models (that include the sub-groups of those variables), it can be


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claimed that the negative sign results from hidden multicollinearity among the

explanatory variables. 27 However, this result indicates that larger firms tend to provide a

lower level of KPI disclosure quality than smaller firms. This might be due to the nature

of the KPI information itself, as it was argued that the significance of disclosure

incentives differs according to the disclosure topic or even within the same topic (Beattie

and Smith, 2012). Furthermore, the negative association between firm size and KPI

reporting might be explained by the larger firm’s preference to provide this information

via other means of communication (e.g. conference calls, presentation to financial

analysts, and websites).

Overall, drawing upon the findings of the main analyses, two groups of variables appear

to have a significant impact on KPI reporting quantity and quality: directors’

compensation, and board characteristics variables. In turn, the key effect in terms of

capital need variables is on KPI reporting quantity.

The analyses findings indicate that KPI reporting is not associated with other firm

characteristics. The non-significant results with regard to these characteristics - which are

commonly used as controls in previous research - shed light on the important role of CG

mechanisms and directors’ compensation as drivers for corporate disclosure. According

to the main analyses findings, focusing on these factors would motivate UK firms to

increase the amount and quality of KPI reported in the KPI section in the annual report.

Concerning the analyses that investigate the determinants of KPI subcategories, the

results suggest that the factors proposed in the current study could explain the variation in

financial and non-financial KPI reporting.

27

The researcher is grateful to the external examiner Dr Bassil Al-Najjar for this clarification.


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Regarding the findings with respect to factors that affect financial KPI reporting, these

findings are important as the majority of companies place stress on disclosing financial

KPIs. The results are consistent with agency and signalling theories. Directors with high

levels of compensation, larger boards with a high percentage of NEDs tend to improve

QNFKS and QLFKS in order to show their action in support of shareholders’ interests,

and to signal themselves as talented directors in the employment market. Additionally,

agency theory, stakeholder theory, and capital need theory can be used to interpret the

positive relationship between firms’ plans to obtain loans and QNFKS as well as QLFKS.

The results also reveal that QNFKS and QLFKS are not derived from identical set of

determinants.

The findings suggest that, in line with agency theory, the monitoring power of active AC

lead to an increase in QLFKS. Correspondingly, role duality shows a negative effect on

QLFKS as it works against the monitoring role of the board. Finally, the positive

relationship between managerial ownership and QLFKS can be explained by the

alignment of interests among managers and shareholders when managers own a relatively

large stake in the firm (Jensen and Meckling, 1976).

With respect to non-financial KPI reporting determinants, the results suggest that CG

mechanisms that are related to board characteristics play a key monitoring role, which in

turn, lead to higher levels of QNNFKSEC and QLNFKSEC. In addition, the findings

provide evidence that incentives to increase QNNFKSEC depend on the source for

raising capital. Firms show a tendency to increase the quantity and quality of non-

financial KPI reporting when they are planning to seek loans. In contrast, they limit

QNNFKSEC in the event of getting funds through issuing equity. This finding could be

explained by capital need and stakeholder theories; directors may use reporting on non-

financial KPIs in order to provide debtors with a complete picture regarding corporate


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strategy and performance. In contrast, directors may prefer to focus on providing

financial performance information when they communicate with shareholders with regard

to increasing their stake in the firm.

3.8 Further analysis

As mentioned before, the research instrument is designed to collect and score other KPIs

that are disclosed in other parts of the annual report rather than in the KPI section. This

procedure mainly affects the non-financial KPI reporting scores, and the total KPI

reporting scores. As a result, new dependent variables are introduced. These variables

are: QNNFKREP, which represents the number of non-financial KPIs disclosed in the

whole report (those in the KPI section + those in other parts of the report), QNTKREP

which represents the total number of financial and non-financial KPIs that are disclosed

in the whole report, QLNFKREP which is the quality score of non-financial KPIs

disclosed in the whole report, and QLTKREP which represents the aggregated quality

score of financial and non-financial KPIs that are disclosed in the whole report.

In a further step of the analyses, the KPIs that are reported in other sections in the annual

report are considered. These analyses add more depth to the study, as they show the

extent to which the main findings are robust when considering these KPIs.28

3.8.1 The determinants of total non-financial KPI reporting

Table 63 Table 64 in Appendix (2) present the results with regard to the key determinants

affecting reporting on non-financial KPIs disclosed in the whole report. The models

proposed can explain 17.3% to 23.3% of the variation in QNNFKREP, and explain

10.8% to 21.6% of the total variation in QNNFKREP. Furthermore, there are no concerns

28

To check the robustness of these results, all models reported in Table 59, Table 60, Table 61, and Table

62 are re-estimated using different measurements for some of the firm characteristics variables. The results

confirm the findings discussed in 3.6.2.1


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regarding multicollinearity between the determinants of QNNFKREP and QLNFKREP

according to VIF values.

Regarding the results of these analyses, it seems that the majority of explanatory

variables continue to have the same relationship with reporting on non-financial KPIs in

the analyses that investigate the determinants of QNNFKSEC and QLNFKSEC.29 In

addition, Table 63 reveals that executives’ compensation show a weak positive effect in

terms of QNNFKREP, while major shareholding appears to have a negative influence on

it at a significance level of 10%. On the other hand, a weak positive relationship is

observed between AC meetings and QLNFKREP (as reported in model 4 in Table 64).

To conclude, these results are very close to the results in the analyses that investigate the

determinants of QNNFKSEC and QLNFKSEC. Hence, they add to the robustness of the

findings with regard to the determinants of non-financial KPI reporting.

3.8.2 The determinants of total KPI reporting

Table 65 and Table 66 in Appendix (2) report the analyses’ findings which indicate the

main factors affecting reporting on KPIs disclosed in the whole report in terms of

quantity (QNTKREP) and quality (QLTKREP). The findings show that the proposed

explanatory variables explain a significant part of QNTKREP and QLTKREP. The

adjusted R2 values indicate that the models proposed can explain 15.5% to 24.1% of the

variation in QNTKREP, and explain 12.1% to 27.3% of the total variation in QLTKREP.

In short, the findings are consistent with the results produced in the main analyses which

focus on the KPIs disclosed in the KPI section.30 Compared with the main analyses, it is

observed that the executives’ compensation variable has become significant in its positive

29

Look at Table 61 and Table 62 for further details. 30

Look at Table 24 and Table 25 for further details.


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effect on KPI reporting quantity at a level of 5% (as reported in model 1 and model 7 of

Table 65).

For KPI reporting quality, Table 66 illustrates the results are almost as the same as those

produced in the main analyses. However, it is revealed that managerial ownership shows

a weak positive effect on QLTKREP.

3.9 Conclusion

This study provides answers to research questions Q2 and Q3; it examines factors

affecting the level of quantity and quality of KPI reporting in the UK (Q2). It also links

the findings to question the validity of using the quantity of disclosure as a proxy for

quality in accounting studies (Q4).

Arguably, KPI information is disclosed on voluntarily basis. Directors are able to control

the level and the quality of KPI reporting thanks to the elastic nature of the regulations.

The study draws upon different theories that explain directors’ motivations to disseminate

more information. Consequently, the determinants of KPI reporting quantity and quality

are proposed and sorted into six groups of variables. Panel data regressions are conducted

to test the hypotheses of the study. Focusing on KPIs that are disclosed in the KPI

section, the findings show that there is a significant relationship between board

characteristics and KPI reporting. In particular, board size, board composition, non-

executives’ compensation, and firm’s plans to acquire loans, have a significant and

positive relationship with the quantity and quality of KPI reporting. In contrast, role

duality has a negative influence on both of them.

Given the study objective to examine whether the quantity and quality of KPI reporting

are derived from the same factors, it is observed that they are not identically derived from


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the same factors. The study reports that executives’ compensation and the number of AC

meetings have a positive impact on the quality of KPI disclosures rather than their

quantity. On the other hand, a firm intending to issue bonds is significantly and positively

related with the quantity of KPIs disclosed in the KPI section, whereas board meetings

have a negative association with it. These results question the proposition of using the

quantity of disclosure as a proxy for its quality in most accounting studies.

Unlike previous research, it is documented that no firm characteristics have an influence

on KPI disclosure with the exception of firm size which has a significant effect on KPI

quality. These empirical results are confirmed by investigating the determinants of KPI

subcategories. The findings of these analyses are consistent with the above results,

suggesting that the factors proposed in the present study can explain the variation in

quantity and quality of financial and non-financial KPI reporting.

To add to the robustness of the results, KPIs disclosed outside the KPI section are

considered in further analyses. The findings of these further analyses are too close to

those of the main analyses that investigate the determinants of KPI reporting for the KPIs


In short, CG mechanisms and directors’ compensation appear as the key drivers towards

higher levels of KPI reporting in terms of quantity and quality. This places a stress on the

need to improve CG mechanisms in UK firms. Moreover, the findings provide strong

evidence that shareholders could affect the quantity and quality of KPI reporting by

offering higher compensation for non-executives.

CHAPTER FOUR: THE IMPACT OF KPI REPORTING ON FIRM VALUE

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Chapter 4 - The impact of KPI reporting on firm value

4.1 Overview

KPI reporting could provide many types of information to the public. Some of this

information is neither included in the financial statements, nor clearly presented in these

statements. KPI reporting could include non-financial information such as the number

of new stores, the number of senior appointments secured internally, recordable

incident rates, and total emissions. Furthermore, according to ASB (2006), KPI

information could contain forward-looking information that shows the targets for these

KPIs as well as management commentary on these targets.

As indicated in the previous chapter, KPI information is available to insiders, and they

decide to disclose it for many purposes (i.e. signalling). High quality KPI information

could provide a better picture of business performance and progress to the investors.

Information with respect to current and future performance can be compared with that

of competitors. Given the variation between UK firms in terms of the quantity and

quality of KPI reporting discussed earlier, it could be expected that KPI reporting could

have an impact on firm value in the UK.

In general, the previous literature provides mixed results about the relationship between

corporate disclosure and firm value. In addition, previous studies place more focus on

the impact of providing a higher quantity of disclosure on firm value. This chapter aims

to provide answers to research questions Q3 and Q4. It extends the literature by

examining the effects of KPI reporting on firm value in the UK (Q3). Furthermore, it

investigates whether the quantity and quality of KPI reporting have a different influence

on firm valuation. Hence, the analyses carried out provide evidence with regard to the

validity of using the quantity of disclosure as a proxy for its quality in accounting

studies (Q4). Moreover, further analyses in this chapter provide evidence with regard to


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how different types of KPI information (i.e. financial KPIs, and non- financial KPIs)

could have a different impact on firm value.

The remainder of this chapter is organised as follows: section 4.2 provides the

theoretical basis for explaining the value relevance of corporate disclosure, as well as

reviewing previous relevant literature. Section 4.3 develops the hypotheses of the study.

Section 4.4 illustrates the data, provides the data in terms of descriptive statistics, and

introduces the regression models employed. Section 4.5 contains the main analyses, and

a discussion of the empirical results. Section 4.6 reports the empirical findings with

regard to the impact of KPI subcategories reporting. Finally, section 4.7 reports the

conclusion of this part of the study.

4.2 Theory and previous literature

4.2.1 Theory

This chapter mainly focuses on examining whether disclosing more and higher quality

KPI information affects firm value. The association between corporate disclosure and

firm value can be explained through agency theory and the efficient market hypothesis.

Theoretically, firm value is increased as a result of enhanced disclosure levels through

either reducing the cost of capital or increasing the cash flow to its shareholders, or both

(Amihud and Mendelson, 1986; Diamond and Verrecchia, 1991). Arguably, high

disclosure levels would reduce the cost of capital because it leads investors to reduce

their estimation of the risk level, and therefore reduces the required rate of return when

acquiring a firm’s shares (Coles et al., 1995; Clarkson et al., 1996). Additionally, the

value of the firm would be increased after the anticipated increase in stock liquidity as

the transaction costs would be reduced, and the demand for firm’s shares would be

increased (Amihud and Mendelson, 1986; Diamond and Verrecchia, 1991).


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As discussed before, the information asymmetry problem and agency conflicts could

exist among company managers and outsider stakeholders (Healy and Palepu, 2001).

Outsiders usually do not have access to internal information about the company which

is available to managers. This could have an impact on the external shareholders’

expectations regarding risk, required returns, and company cost of capital, and thus its

share value. Hence, enhanced corporate disclosure is a tool used to mitigate these

problems (Hassan, 2009).

Healy and Palepu (1993) claimed that the greater the disclosure level, the higher the

possibility that investors understand managers’ business strategies. Reducing the

information asymmetry between management and uninformed investors would result in

a reduction in the uncertainty surrounding the future performance of the firm, and an

increase in its shares’ liquidity (Diamond and Verrecchia, 1991). Consequently, the

lower transaction costs as well as the higher demand for firm’s shares could result in an

increase in share price and hence the value of the firm would increase (Coles et al.,

1995; Clarkson et al., 1996). On the other hand, a high demand for firm’s shares could

lead to a reduction in the firm’s cost with regard to equity capital (Diamond and

Verrecchia, 1991; Healy and Palepu, 1993).

However, these potential effects of enhanced disclosure may not be feasible because

enhanced disclosure could have a negative effect upon the competitiveness of the firm

(Healy and Palepu, 1993), and therefore one might anticipate that additional disclosure

might have adverse effects on a firm’s valuation.

Furthermore, Chung et al. (2012) argued that increasing the amount of disclosure,

regardless of its quality, may not affect the capital market, suggesting that an

incremental amount of disclosure increases the level of noise. Poor quality disclosure

causes a lack of investor confidence, which could lead to difficulties in getting funds


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from capital markets (Clarkson et al., 2004). In contrast, high quality reporting impacts

positively on a firm’s value as a result of attracting institutional investors and incurring

lower capital costs (Dhaliwal et al., 2011).

KPI reporting provides information that helps different stakeholders to assess the

current and future performance of a firm with regard to operating, social and

environmental activities. However, KPI information offers clear measures to evaluate

positive and negative developments in the firm (Boesso and Kumar, 2007). Therefore,

the quantity and quality of KPI disclosures might have different effects on firm

valuation.

Arguably, KPI information would mitigate the information asymmetry problem. It may

reduce the uncertainty of firms' future performance, and hence increase stock liquidity

which positively affects firm value.

Thus, if KPI information is provided by a firm with a lower level of reporting quality,

investors’ concerns about the credibility of this information would be increased.

Consequently, the shares of this firm could be mispriced or undervalued by investors.

Additionally, higher quantity KPI information might lead to negative effects on a firm’s

valuation if it displays a negative picture of the firm’s social and environmental

performance. Investors might overestimate the costs and risks related to these activities

which might negatively influence their expectations with regard to future performance,

and consequently their expectation about the firm’s value. In this case, high quality KPI

information could support a negative influence on investors’ perceptions.

On the other hand, in line with the efficient market hypothesis, share prices are adjusted

in line with the information available. According to Fama (1970), in an efficient stock

market, information is incorporated in the share prices. Therefore, there is no chance of


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outperforming the market by predicting future share prices either through technical or

fundamental analyses (Malkiel, 2003). Thus, Fama (1970) shows that there are three

forms of stock market in terms of efficiency: strong market efficiency, semi-strong

market efficiency, and weak form of market efficiency.

First, in a strong efficient market, all available (public and private) information is fully

reflected in share prices. Second, in a semi-strong efficient market, share price is

adjusted based on public information disclosed about the firm. Finally, in a weak form

of efficient market, share prices follow a random walk which is independent of the

historical information that is available about prices.

However, it is argued that markets are not perfectly efficient. For instance, it is stated

that:

‘By the start of the twenty first century, the intellectual dominance of the efficient

market hypothesis had become far less universal. Many financial economists and

statisticians began to believe that stock prices are at least partially predictable. A new

breed of economists emphasized psychological and behavioural elements of stock-price

determination, and they came to believe that future stock prices are somewhat

predictable on the basis of past stock price patterns as well as certain “fundamental”

valuation metrics. Moreover, many of these economists were even making the far more

controversial claim that these predictable patterns enable investors to earn excess risk

adjusted rates of return’ (Malkiel, 2003, p.60).

In reference to the current study, KPI disclosures that are published within the annual

report would contain public and private information. It can be argued that, whatever the

form of efficiency of the stock market, share prices would be adjusted as a result of the

release of such information. Therefore, market participants would not be able to

outperform the market and obtain abnormal returns using KPI information.


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Investors would expect that KPI information - provided by the agent - is of high quality

so as to incorporate this information into the stock valuation. However, managers may

act against the interests of shareholders by building their own empires, enjoying perks,

earning from insider trading, or by making inappropriate investments (Schleifer and

Vishny, 1997). Hence, managers might maximise their own wealth at the expense of

shareholder value. Therefore, shareholders need to monitor firm’s disclosures to assess

its quality, and to avoid any misleading information introduced by managers (Patten,

2005).

CG mechanisms are introduced to align the interests of the managers (the agent) and

those of the shareholders (the principal) (Fama and Jensen, 1983). These mechanisms

include incentive schemes that base directors’ compensation on long term return targets

(Jensen and Meckling, 1976). Moreover, CG mechanisms comprise activating the AC

role in monitoring the financial reporting process, improving internal controls, and

maintaining effective risk management. They also include increasing non executive

directors’ representation on the board in order to ensure that executive directors work

on maximizing shareholder value. Finally, management ownership and block holders

might help in reducing the conflict of interest between managers and shareholders.

They carry out an effective role in ensuring that managers are not entrenched (Laporta

et al., 2002; Haniffa and Hudaib, 2006; Setia-Atmaja, 2009).

4.2.2 Previous literature

The purpose of this section is to review the academic literature on the economic

consequences of corporate disclosure, with particular emphasis on its impact upon firm

value. This discussion provides insights on the potential impact of KPI reporting.

Corporate disclosure influences on the stock market is of a great interest in accounting

research. Previous studies have shown that corporate disclosure could affect stock


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market participants’ anticipations and decisions. For instance, many studies have

documented a relationship between disclosure levels and the number of analysts

following the firm, analysts’ forecast accuracy, cost of capital, and the value of the firm

(e.g. Dhaliwal et al., 1979; Firth, 1984; Lang and Lundholm, 1996; Clarkson and

Satterly, 1997; Botosan, 1997; Francis et al., 1997; Rogers and Grant, 1997; Sengupta,

1998; Eng and Teo, 2000; Botosan and Plumlee, 2002, Vanstraelen et al., 2003; Beak et

al., 2004; Wang et al., 2008; Hassan et al., 2009; Yu, 2010 Dhaliwal et al., 2012). The

findings of these studies are, to some extent, related to the current study, as they offer

evidence of the usefulness of some types of information that are also provided as part of

KPI reporting. This information includes forward-looking information and non-

financial information.

On the other hand, the previous studies that investigated the impact of corporate

disclosure on firm value showed mixed results. A number of these studies suggest that

corporate disclosure - in general or by focusing on a particular type - has a positive

association with firm value. For example, Uyar and Kiliç (2012) documented that

voluntary disclosure levels in Turkish annual reports are related to the level of firm

value (measured by market capitalisation). Similarly, Silva and Alves (2004) found that

firm value has a significant and positive relationship with financial information

voluntarily disclosed online by Latin American companies.

In turn, Murray et al. (2006) found no significant impact on the part of social and

environmental disclosure upon the market performance of large UK firms. They

highlighted the need to clarify the theory that explains why investors do not react to

higher levels of social and environmental disclosure.


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Hassan et al. (2009) found that the relationship between the two variables is complex

and closely associated with the type of disclosure (mandatory/voluntary). They reported

that there is no significant association between firm value and voluntary disclosure

made by Egyptian firms, while it has a negative and significant relationship with

mandatory disclosure. Furthermore, Uyar and Kiliç (2012) found that the association

between voluntary disclosure and firm value has been affected by firm value

measurement. In particular, their results changed from positive to being insignificant

when they employed market-to-book value as the dependent variable in the regression

model instead of market capitalisation.

Most previous studies did not focus on the potential effect of disclosure quality upon

firm value. Accounting research usually uses disclosure quantity as a proxy for its

quality (e.g. Hussainey and Mouselli, 2010; Mouselli et al., 2012). According to this

proposition, one can assume an equivalent impact in terms of both disclosure quantity

and quality upon firm value.

Few studies have shown that disclosure quality affects the stock market. These studies

indicate that firms with lower reporting quality could face lower levels of stock returns,

or suffer from mispricing problems (e.g. Healy et al., 1999; Drake et al., 2009; Chung et

al., 2012, Mouselli et al., 2012). Notably, the previous literature that examined the

impact of corporate disclosure quality has used different proxies that might not directly

measure disclosure quality.31

A number of these studies were conducted in the US using the analysts’ disclosure

ratings (AIMR) or the existence of American Depositary receipts (ADR) as proxies for

disclosure quality. For instance, Healy et al. (1999) found that higher reporting quality -

31

For a further detailed review of disclosure quality measures, see section 2.2.2.


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measured by analyst disclosure ratings - are associated with higher stock returns. The

findings suggest that firms with high disclosure quality attract the attention of investors

and analysts and increase stock liquidity.

Baek et al. (2004) study suggested a similar finding in the Korean setting during periods

of Asian crises. They found that firms with higher disclosure quality (proxied by having

ADR)32, experienced a smaller decrease in their value during the financial crises. Chung

et al. (2012) reported that investors in the Korean stock market misprice firms that issue

derivative-related loss announcements of low disclosure quality (i.e. that issue the

information on the underlying foreign currency subsequent to the announcement date).

In the UK, Mouselli et al. (2012) showed that disclosure quality (measured by the

number of future-oriented statements) can explain variations in stock returns.

To the best of the author’s knowledge, there is no previous study that directly tested the

effects of KPI reporting on firm value. KPI information includes different types of

information that might attract stock market participants. KPI reporting provides

financial and non-financial information that evaluates firm performance. KPI

disclosures also present past and future-oriented information. This information is not

only limited to financial performance, but also is related to social and environmental

performance.

Therefore, the current study builds on previous research that generally showed the

importance of information included within KPI disclosures for stock market

participants. For instance, previous literature provides evidence of financial analysts

focusing on certain types of information such as non-financial information (e.g. Epstein

32 The existence of American Depositary Receipts (ADR) refers to non-US companies that are listed on

the US stock market(s). ADR is used as a proxy of disclosure quality because of their commitment to

enhance disclosure levels.


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and Palepu, 1999; Breton and Taffler, 2001; Beattie and Pratt, 2002). Other studies

highlighted the importance of narrative reporting to sell-side financial analysts, such as

financial and non-financial indicators (Dempsey et al., 1997), and non-financial

forward-looking information (Hussainey and Walker, 2006).

Haggard et al. (2008) argued that investors value credible and enhanced firm-specific

information that is voluntarily disclosed by firms. The study indicates that firms that

make this information easy to access by investors would avoid price crashes. Booker et

al. (2011) conducted an experimental study that highlights the role of the content of

non-financial performance indicator narratives on users’ perceptions.

Empirically, Mavrinac and Seisfeld (1997) provided evidence of investor reliance upon

non-financial data. In particular, they showed that investors express their interest in

non-financial factors that could shape firms’ performance in the future. These factors

include product quality, the perceived quality of management, investment in employee

development, and the corporate innovations. Therefore, it can be expected that firms

that show clearly to the public any improvements in their KPI results concerning such

factors could add to their shareholder value. Ittner and Larcker (1998) found that non-

financial customer satisfaction indicators are value relevant to the stock market. They

argued that disclosing these measures provide information to the market on expected

cash flows. Thus, they found that releasing these measures is statistically associated

with excess stock return over a ten day announcement period. However, Barton et al.

(2010) examined the value relevance of some financial indicators including sales,

EBITDA, operating income, income before income taxes, income before extraordinary

items and discontinued operations, net income, total comprehensive income, and

operating cash flows. They found that these indicators displayed value relevance in 46


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countries during 1996–2005. No single indicator gained dominance in all countries.

However, investors showed higher value towards those indicators that include core

operating expenses and exclude more transitory items like extraordinary items, gains

and losses, and other comprehensive income (i.e., operating income and EBITDA). On

the other hand, Dorestani and Rezaee (2011a) examined the role played by non-

financial KPIs in analysts’ forecasts. They investigated the association between non-

financial KPI disclosures and the accuracy of analysts’ forecasts for a sample of US

firms for the two-year period between 2006 and 2007. Their results suggest that the

change in KPI quantity (measured by the ratio of the total number of KPI keywords

disclosed to total words included in management discussion and analysis) does not have

a significant impact on analysts’ forecast accuracy (measured by forecast errors).

Dorestani and Rezaee (2011b) found a positive association between the quantity of non-

financial KPIs disclosed and the perceptions of investors about quality of earnings

(measured by a factor that captures the association between current accruals and cash

flows). Yet, they found this relationship significant only for Oil and Gas companies.

However, Dorestani and Rezaee’s studies examine the effects of non-financial KPI

reporting quantity rather than its quality. They also did not consider financial KPI

reporting in their analyses. Looking at UK studies that explored the importance of KPIs

for stock market participants, only Hussainey and Walker (2006) investigated to what

extent analysts’ reports could rely on KPI disclosure. Their study gives a good

indication that financial analysts depend on different KPIs among high and low growth

UK companies. However, the study did not investigate the impact of KPI disclosures on

firm valuation.


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To conclude, the results of previous studies regarding the association between corporate

disclosure and corporate value are mixed. It is argued that this association is sensitive to

the type of disclosure, and the proxy employed for firm value. This suggests that the

relationship between corporate disclosure and firm value is still an open empirical

question. Additionally, a limited number of studies have investigated the impact of

disclosure quality upon the stock market. None of these studies has examined the

relationship between disclosure quality (considering the qualitative characteristics of

information disclosed) and firm value. Although previous studies have shown that

market participants pay attention to many types of information that can be included in

KPI disclosures, there is a lack of evidence on KPI reporting association with firm

value.

The variation among UK firms’ KPI reporting in practice provides a good research

avenue to test the association between KPI reporting and firm value. As mentioned

earlier, this type of disclosure is mandated, but the regulations allow firms’ directors to

control it. Furthermore, although CG attributes are found to affect firm value in some

previous studies (e.g. Klein, 1998; Haniffa and Hudaib, 2006; Aggarwal et al., 2009),

the nature of CG regulations has a similar orientation. Firms are allowed to comply with

the CG code or just explain the reasons for not complying with it. Therefore, it is

interesting to examine how firm value could be affected by corporate KPI reporting in

this unique setting.

In addition, the current study seeks to fill the following gap in the literature. As

mentioned earlier, low quality incremental information could cause noise to stock

market participants. Several studies reveal that disclosure quality is valued by investors

(e.g. Baek et al., 2004). In contrast to the previous studies that used disclosure quantity


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as a proxy for its quality, the current study challenges this proposition by measuring

KPI reporting quality by considering all qualitative attributes of the information implied

by the ASB (2006) guidance for best practice.

This study would extend the literature on the relationship between firm’s value and

corporate disclosure in general, and KPI reporting specifically. To the best of the

author’s knowledge, there is no study - either in the UK context or in any other context

- that has examined whether the quantity of KPI disclosure and its quality have different

relationships with firm value.

4.3 Research hypotheses

The aim of the current study is to test the effect of KPI quantity as well as KPI reporting

quality on firm value (Q3). This helps to examine whether the quantity and quality of

KPI disclosure have the same relationship with firm value, and hence, it could also help

in examining whether disclosure quantity can be used as a proxy for its quality (Q4).

4.3.1 KPI reporting quantity and quality and firm value

The literature on the impact of corporate disclosure on firm value is limited and offers

mixed results. In addition, most of these studies have focused on the impact of

disclosure quantity regardless of its quality. This provides a good opportunity to analyse

the relationship between KPI reporting including its subcategories and firm value.

KPI disclosures include different types of information that might be of interest to

investors including financial indicators about firm operating activities, financial

performance, and capital expenditure. Moreover, KPI information might contain non-

financial indicators related to firm performance such as social and environmental

activities.

From the agency theory perspective, since KPI reporting may provide investors with


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data that is available to insiders, one could expect that KPI disclosures would mitigate

the information asymmetry problem. Information on financial and non-financial KPI

would reduce uncertainty about business performance. Therefore, increasing the

quantity of KPIs disclosed could help the investor to evaluate the success of

management in terms of achieving a firm’s objectives, and also to evaluate future

prospects. It is argued that more released information might lead to an increase in the

demand for corporate shares and, in turn, its price (Healy and Palepu, 1993; Clarkson et

al., 2013).

Some studies suggest a positive association between the quantity of disclosure and the

value of the firm. For example, Healy et al. (1999) found that firms with higher levels

of voluntary disclosure are more likely to show an increase in stock price, regardless of

their earnings.

Therefore, one can expect that the higher the quantity of KPIs disclosed, the higher the

market value of the firm. However, enhanced disclosure could have adverse effects if it

puts the firm at a competitive disadvantage compared with its rivals (Healy and Palepu,

1993; Hassan et al., 2009), or if it makes more noise to the investors which negatively

affects their valuation (Chung et al., 2012). As mentioned before, the extant literature

does not present strong evidence on the positive association between corporate

disclosure and firm value. Furthermore, it is found that the direction and magnitude of

the relationship is associated with the type of disclosure (Hassan et al., 2009) and the

proxy that is used for firm value (Uyar and Kiliç, 2012).

On the other hand, several studies show that the quality of different types of disclosure

is relevant and important to the users. For example, it can improve share price

anticipation of earnings (Hussainey et al., 2003; Schleicher et al., 2007, Hussainey and

Walker, 2009). It is has also been shown that disclosure quality can be used


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interchangeably with other information sources such as dividend propensity (Hussainey

and Walker, 2009), and accruals quality (Mouselli et al., 2012). Regardless of the

approach used to measure disclosure quality in these studies, it can be argued that

reporting quality can be employed to affect the perceptions of stock market participants

about firm performance. Gelb and Zarowin (2002) found that the relationship between

stock price and firm’s earning performance is stronger in firms with higher disclosure

quality, in contrast to firms with low levels of disclosure quality (measured by

disclosure ratings).

In general, the previous studies did not directly examine the impact of disclosure quality

- as opposed to its quantity - on firm value. One exception was the study of Gelb and

Zarowin (2002) that used analyst ratings as an indication of disclosure quality.

However, previous studies used the amount of disclosure as a proxy for its quality.

Their results are more relevant with regard to explaining the different impacts of

disclosure quantity.

Taking into consideration that KPI information might include new and incremental

information which is not included in the financial statements; investors might reflect

this information in their share prices assessment. For instance, KPI information might

provide the investors with future prospects of financial as well as non-financial KPIs

and their relationship with the strategy of the firm. It also might include non-financial

KPI information about environmental aspects such as recycling rates, emissions of

manufacturing operations, and environmental accidents. In addition, KPI reporting may

also provide vital information about the workforce such as staff attrition rate, and the

number of senior appointments sourced internally. Therefore, it is expected that

disseminating KPI information would lead to share prices adjustments in accordance

with the efficient market hypothesis. However, stock market participants will not be


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able to achieve abnormal returns relying on KPI information. In other words, KPI

information would affect firm value, but without outperforming the market and yielding

abnormal returns.

As mentioned before, markets do not seem to be perfectly efficient. For example, Ball

(1978) documented the occurrence of drifts in share price after the announcement of

new information. He argued that markets are not efficient in processing new

information, and hence, this information was not incorporated in adjusting share prices.

Furthermore, Owusu-Ansah (2000) pointed to the importance of the timeliness of

annual reports. He claimed that accounting information - disclosed in the annual report -

could be useless to the users if the annual report is published too late.

Therefore, it can be argued that KPI information would affect share prices based upon

these considerations. First, this information should not be published too late. Second,

KPI information should be of high quality in order for it to be comprehended and

incorporated correctly in share prices by investors.

On balance, previous studies lead to the prediction of a relationship between KPI

reporting and firm value. Taking into consideration the highly positive relationship

between KPI reporting quantity and KPI reporting quality, one can expect that each of

them has a similar effect upon firm value. However, given the contradictory and limited

evidence in previous studies regarding corporate disclosure association with firm value

in the UK, the current study investigates the direction and extent of the relationship

between KPI reporting and firm value using a different measure to assess KPI

disclosure quality. This would help to test whether KPI reporting quantity and its

quality can work as substitutes. Therefore, it is hypothesised that:

H1: There is a significant relationship between KPI reporting and firm value.


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H1a: There is a significant relationship between KPI reporting quantity and firm value.

H1b: There is a significant relationship between KPI reporting quality and firm value.

4.3.2 Corporate governance mechanisms and firm value

Drawing upon agency theory, the general objective of CG mechanisms is to align

managers’ interests with shareholders’ interests. It is anticipated that CG mechanisms

motivate managers to increase shareholder’s value (Bruce et al., 2007). According to

previous studies, it is expected that various factors have an impact on firm value.

Therefore, this study considers the impact of CG mechanisms - proposed by previous

studies - on firm value (e.g. Morck et al., 1988; Laporta et al., 2002; Haniffa and

Hudaib, 2006 Hassan et al., 2009; Aggarwal, 2009; Setia-Atmaja, 2009; Ezat, 2010;

Ammann et al., 2011; Ujunwa, 2012). To facilitate forming and testing the hypotheses,

CG variables are classified into the following categories: Directors’ compensation in

section 4.3.2.1, Board characteristics in section 4.3.2.2, AC characteristics in section

4.3.2.3, and Ownership structure in section 4.3.2.4.

4.3.2.1 Directors’ compensation

Al-Najjar et al. (2011) showed that high CEO compensation reflects high managerial

talent which leads to making value added decisions for shareholders (e.g. cutting capital

and M&A expenditures). According to agency theory, incentive plans are designed to

encourage board directors to maximize shareholders’ wealth (Jensen and Meckling,

1976). That is achieved by linking directors’ compensation with the financial objectives

of the firm. Hence, it can be expected that high directors’ remuneration could be

associated with higher firm performance. Bruce et al. (2007) claimed that bonus

schemes makes executives perceive that they are being monitored. Al-Najjar et al.

(2011) found that higher compensation paid to CEOs is positively associated with firm


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performance as measured by Tobin’s Q for UK firms over the period from 2003 to

2009.

However, to the best of the author’s knowledge, the relationship between total

compensation - either for executive or non-executive directors - and firm value has not

been investigated in the literature. Therefore, the current study addresses this issue and

the following hypotheses are formulated:

H2. An association exists between executive compensation and firm value.

H3. An association exists between non-executive compensation and firm value.

4.3.2.2 Board characteristics

4.3.2.2.1 Board size

It is claimed that the combined experience and knowledge of board members is

essential in today’s complex business environment (Conger et al., 1998). Hence, it is

argued that a large board could help the firm to secure critical resources that positively

impact on its financial performance (Dalton et al., 1999). However, previous studies

have shown inconsistent results with regard to board size effect on firm performance or

value (Goodstein et al., 1994; Yermack, 1996; Kiel and Nicholson, 2003; Haniffa and

Hudaib, 2006; Ezat, 2010; Ujunwa, 2012). Thus, this relationship between board size

and firm value is examined. The following hypothesis is formulated:

H4: There is a significant relationship between board size and firm value.

4.3.2.2.2 Board composition

Boards with a high proportion of NEDs can reduce the agency problem. Managers are

usually dominated by their self-interest targets at the expense of shareholders.

Therefore, boards with a high proportion of NEDs could better perform monitoring


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roles (Dalton et al., 1999). Hence, it appears that this factor could have a significant

effect on investment decisions and hence on firm value. However, there are some

disadvantages of boards that are dominated by NEDs. In particular, such boards might

suffer from the lack of strategic decisions (Goodstein et al., 1994), in addition to the

lack of local experience and training of outsider directors in contrast to insiders (Dalton

et al., 1999). Previous studies showed mixed findings regarding the association between

board composition and firm value (Agrawal and Knoeber, 2001; Haniffa and Hudaib,

2006; Aggarwal et al., 2009; Setia-Atmaja, 2009). Therefore, the following hypothesis

is formulated:

H5: There is a significant relationship between board composition and firm value.

4.3.2.2.3 Board meetings

In general, previous research does not provide clear evidence of the relationship

between board meetings and firm value. Thus, it is suggested that boards with more

frequent meetings are superior when it comes to setting a firm’s strategy and

monitoring managers’ performance (Conger et al., 1998). In reference to agency theory,

this could reduce the agency problem and, in turn, would affect shareholders’ wealth.

Therefore, the following hypothesis is formulated:

H6: There is a significant relationship between board meetings and firm value.

4.3.2.2.4 Role duality

According to agency theory, if the CEO of the firm holds the chairman position, board

monitoring will be impaired. This situation would lead to CEO entrenchment which

negatively affects the firm’s financial performance (Chen at al., 2008; Ujunwa, 2012).

However, several studies failed to find evidence that firm value is significantly affected

by CEO duality (e.g. Haniffa and Hudaib, 2006; Aggarwal et al., 2009). It can be


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argued that the concentration of power will enable the CEO - who has enough

experience and knowledge about the operating and financial activities of the firm - to

control the firm and improve its performance. Hence, this duality might have a positive

impact on firm value. Therefore, the role duality effect on firm value will be

investigated in the current study. Thus, the following hypothesis is formulated:

H7: There is a significant relationship between role duality and firm value.

4.3.2.3 Audit committee characteristics

As one of the main CG mechanisms, the AC role is to oversee financial reporting, the

internal control system and risk management in the firm. It is anticipated that effective

ACs which have enough human and time resources would have more ability to

undertake its responsibilities, and protect shareholders’ interests. Arguably, the larger

ACs and the more active ACs would be associated with better firm valuation.

Nevertheless, previous empirical studies have not provided evidence that directly

supports this argument. In contrast, most of these studies focused on the association

between AC characteristics and financial reporting quality (e.g. Klein, 2002; Carcello

and Neal, 2003).

The next hypotheses are formulated as follows:

H8: There is a significant relationship between AC size and firm value.

H9: There is a significant relationship between AC meetings and firm value.

4.3.2.4 Ownership structure

4.3.2.4.1 Managerial ownership

Managerial ownership is considered as one of the CG mechanisms that reduces agency

conflicts between managers and shareholders (Jensen and Meckling, 1976). In this


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regard, Cheng et al. (2012) claimed that managerial ownership can operate as an

alternative for board monitoring mechanisms. The previous literature has shown mixed

results with respect to managerial ownership’s impact on firm value. For instance,

Haniffa and Hudaib (2006) found that there is no significant relationship between

managerial ownership and firm value. Thus, Morck et al. (1988) indicated that at

relatively low and high levels of ownership, firm value is positively associated with

managerial ownership. Yet, this relationship turns out to be negative at the medium

level of ownership. On the other hand, Cheng et al. (2012) found that management

entrenchment causes the association between managerial ownership and firm value to

be negative at low and high levels of ownership. In turn, this relationship becomes

negative between the two variables at the medium level of ownership due to a

convergence of interests effect. To test the relationship between managerial ownership

and firm value, the following hypothesis is formulated:

H10: There is a significant relationship between managerial ownership and firm value.

4.3.2.4.2 Block holders’ ownership

Block holders’ ownership may have different effects on firm valuation. La Porta et al.

(2002) indicated that controlling shareholders are willing to pay more for financial

securities when they feel that their rights are better protected. Hence, they improve their

valuation of a firm, as they know that most of its profits will come back to them. On the

other hand, controlling shareholders might raise another agency problem. It can be

expected that in the event of poor investor protection and/or weak CG in a firm, block

holders might expropriate minority shareholders (Shleifer and Vishny, 1997; La Porta et

al., 2002). In such a case, investors might lower their valuation of the firm. The

relationship between block holders and firm value is investigated as the outcomes of

empirical work on the block holders’ valuation effect are mixed. A positive relationship


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in terms of block holders’ ownership and firm value is documented in some studies (e.g.

La Porta et al., 2002; Aggarwal et al., 2009), whereas a negative association is reported

in other studies (e.g. Haniffa and Hudaib, 2006; Ezat, 2010). The following hypothesis

is formulated:

H11: There is a significant relationship between block holders’ ownership and firm

value.

4.3.3 Firm characteristics and other control variables

Following previous studies (e.g. Lins, 2003; Aggarwal et al., 2009; Hassan et al., 2009;

Cheng et al., 2012), this study deals with several firm characteristics. These controls

are: firm size, profitability, leverage, cross listing, cash to assets ratio, capital

expenditure to assets ratio, and property, plant, and equipment to sales ratio.33 Larger

firms have more resources than smaller firms. Therefore, a positive association was

reported between firm size and firm value in several previous studies (e.g. Hassan et al.,

2009; Ezat, 2010). With regard to profitability, firms that report higher profits would

signal their capabilities to the investors. It might be perceived that these firms have

competitive advantages that enable them to achieve higher profits which positively

affect shareholder value. In addition, profitable firms are perceived as firms with more

growth opportunities. Hence, in line with Hassan et al. (2009), a positive relationship is

also expected between profitability levels and firm value. With respect to leverage,

Hodgson and Stevenson-Clarke (2000) stated that high leverage could lead to positive

change in firm value for two reasons. First, tax deductibility on borrowing causes

decrease in the cost of debt, and in turn increases firm value. Second, managers in

highly leveraged companies send good signal to the investors in terms of their

33

Some variables used in the second study are excluded because of collinearity with the suggest ed

variables in the third study such as liquidity. Therefore, the variables used in the study of KPI reporting

relationship with firm value - especially the control ones – are, to some extent, different from those used

in the study of KPI reporting determinants.


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confidence in the firm’s ability to cover debt costs in the future. Moreover, cross listing

is considered as a control variable in the current study. Listing in foreign markets

facilitates firms’ access to alternative sources of finance. Cross listing also positively

affects the liquidity of firms’ shares (Hope, 2003). Thus, a positive association is

expected between cross listing and firm value. Finally, investors usually consider the

information related to firms’ current operations and future growth opportunities before

improving or lowering their valuation of these firms. A firm with higher possibilities of

growth would attract more investors. Therefore, following many studies (e.g. Aggarwal

et al., 2009), the study uses the cash to assets ratio, the capital expenditure to assets

ratio, and the property, plant, and equipment to sales ratio as proxies for current

operations, resources and future growth opportunities.

4.4 Data, regression models, and descriptive statistics

This section presents the data collected, the models employed, and the definition of all

the variables used. It also shows the data descriptive statistics. Panel regressions are

employed in order to examine the relationship between firm value and KPI reporting as

well as other explanatory variables. Section 4.4.1 introduces the sample and the

variables used in the current study. Then, section 4.4.2 introduces the regression

models. Finally, descriptive statistics for the variables used in the current study are

presented in 4.4.3.

4.4.1 The data

As mentioned earlier, the present study focuses on the annual reports for a sample of

UK, FTSE 350, non-financial firms over a five year period (2006-2010). The study

sample is the same sample used in the previous chapters. It consists of 515 annual


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reports of 103 firms published between 2006 and 2010. 34Various observations are

excluded for the reasons illustrated in Panel (A) in Table 30, to come up with 485

observations as the final sample. Panel (B) in the same table provides a disaggregation

of the sample across industries.

Table 30 Sample selection and its disaggregation across industries


Starting point: Top 350 UK firms based on market capitalisation, according to the 2011 Financial Times ranking. Financial firms are then excluded. Subsequently, 103 firms

are selected randomly following two criteria: 1) each sector is represented in the same proportion as in the starting sample; 2) as firms are arranged according to market

capitalisation; systematic sampling is used by choosing the first company in every sector as a starting point. Then, selection is continued by selecting the third, the fifth and so on. Then, selection is continued by selecting the third, the fifth and so on. This

process results in 515 observations [103 * 5 years (2006, 2007, 2008, 2009, and 2010)]. Thereafter, the following exclusions take place:

n observations

excluded

thereafter

Reason for exclusion

2 KPI regulation not applicable in 2006 (because of year end date). 4 Missing data on directors’ compensation.

6 Missing CG data. 3 Missing data on data stream.

15 Having negative book value of shareholders’ equity.35


485 final sample



Basic Materials 40 8.25 Consumer Goods 62 12.78

Consumer Services 97 20.00 Health Care 24 4.95 Industrials 143 29.48

Oil & Gas 51 10.52 Technology 38 7.48

Telecommunications 10 2.06 Utilities 20 4.12

TOTAL 485 100.0

34

For more details on the sample firms, please see section 2.4. 35

Excluding firms with negative book value of equity is an essential step in the analyses to avoid

capturing any effects of financial distress (Lins, 2003: p.163).


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4.4.2 The models

According to the theoretical framework, a relationship is expected between KPI

reporting (quantity and quality) and firm value. Empirical studies used different proxies

in order to capture investors’ reactions to the information disclosed. For instance, stock

return has been used in many previous studies to investigate the effect of information

released (e.g. Conover and Wallace, 1995; Healy et al., 1999; Bloomfield and Wilks,

2000; Lang and Lundholm, 2000; Bushee and Leuz, 2005; Hussainey and Mouselli,

2010; Roychowdhury and Sletten, 2012; Tsalavoutas et al., 2012). In addition, Tobin’s

Q has also been used to measure firm value in some studies (e.g. Morck et al., 1988;

Laporta et al., 2002; Haniffa and Hudaib, 2006; Hassan et al., 2009; Aggarwal, 2009;

Setia-Atmaja, 2009; Ezat, 2010). Other studies have used market-to-book value to

reflect the market value of the firm compared with its book value (e.g. Hassan et al.,

2009; Uyar and Kiliç, 2012).

Following previous studies (e.g. Morck et al., 1988; Laporta et al., 2002; Lins, 2003;

Haniffa and Hudaib, 2006; Hassan et al., 2009; Aggarwal, 2009), Tobin’s Q ratio is

used as a measure of the dependent variable (firm value) in the main analysis.

Additionally, market-to-book ratio is used as well to check the robustness of results

(Haniffa and Hudaib, 2006; Hassan et al., 2009).

Tobin’s Q is equal to the ratio of the firm's market value to the replacement cost of its

physical assets (Morck et al., 1988: p. 296), or ‘The ratio of the market value of assets

to their replacement value at the end of the most recent fiscal year’ (La Porta et al.,

2002: p.1156). This ratio implies investors’ perception of the value of a firm by

reflecting this perception on the ratio’s value. If the ratio is larger than one, it will refer


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to an improvement in that firm’s value due to the efficient usage of its resources, and

vice versa (Hassan et al., 2009).

Following Lins (2003), Tobin's Q ratio is computed as:

Tobin’s Q (TQ) = (total assets + market value of equity − total common equity)/total

assets.

Regarding market-to-book ratio (MB), it is measured as the ratio of the market value of

equity to the book value of that equity. This ratio is a good indicator of how a firm is

being valued by investors. If the ratio exceeds 1, it means that the firm is overvalued by

investors and vice versa (Hassan et al., 2009).36

The market value of equity is calculated as the number of outstanding shares at the year

end, multiplied by the market value of the share at three months after the year end. This

procedure is to ensure that share prices are affected by the KPI information that is

released in the annual reports.37

Regarding the explanatory variables, the variable of interest is KPI reporting, including

KPI reporting quantity and KPI reporting quality. Additionally, CG attributes are used

as explanatory variables. Finally, following previous studies (e.g. La Porta et al., 2002;

Haniffa and Hudaib, 2006, Aggarwal et al., 2009; Hassan et al., 2009; Cheng et al.,

2012), several firm characteristics, as well as other control variables, are included in the

model.

Furthermore, the industry effect is considered in the analyses because it could have an

effect on firm valuation. Political cost theory posits that different industries might be

36

A logarithm transformation is used in order to bring the distribution of TQ and MB nearer to normality

(see chapter three: sections 3.5.3 and 3.6.1 for more details on such procedures). 37

Another procedure is followed to check for the robustness of the results by using the market value of

equity six months after the year end in all regression models.


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subject to different political costs (Ball and Foster, 1982). These costs may arise from

the nature of the industries’ activities or from following specific regulations. Moreover,

being in the public eye could push firms within an industry to employ particular

practices or incur extra expenditures, while firms in other industries may not be subject

to these commitments.

Thus, the current study uses these two models:

The main analysis model:

TQ = α+ β1 QNTKSEC + β2 QLTKSEC+ β3 EXCOMP+ β4 NOEXCOMP + β5

BORSIZE+ β6 BORCOMP+ β7 BORMEET+ β8 ROLEDUAL+ β9 ACSIZE+ β10

ACMEET +β11MANGOWN + β12 MAJORSHAR+ firm characteristics and other

control variables+ ε

The model used for robustness purposes:

MB = α+ β1 QNTKSEC + β2 QLTKSEC+ β3 EXCOMP+ β4 NOEXCOMP + β5

BORSIZE+ β6 BORCOMP+ β7 BORMEET+ β8 ROLEDUAL+ β9 ACSIZE+ β10

ACMEET +β11MANGOWN + β12 MAJORSHAR+ firm characteristics and other

control variables+ ε

Whereas:

TQ = TQ+3: which denotes Tobin’s Q ratio three months after the year end.

MB= MB+3: which represents market-to-book ratio three months after the year end.

QNTKSEC= KPI reporting quantity.

QLTKSEC= KPI reporting quality.


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α = the intercept.

β1 …….β 12= Regression coefficients.

ε = Error term

Table 31 illustrates the definition and measurement for each variable of the present

study.

Table 31 Study variables: definitions & measurement


Dependent Variables TQ+3

TobinsQ The natural logarithm of: (total assets (WC02999) + market value of equity three months after the year end - total common equity (WC03501))/ total assets(WC02999)

MB+3 Market-to-book ratio

The natural logarithm of market value of equity (three months after the year end) to book value of equity (WC03501) ratio.

New explanatory Variables38

CASH_ASSETS Cash to total assets ratio

Cash (WC02003) to total assets (WC02999).

CAPEX_ASSETS Capital expenditures to assets ratio

Capital expenditures (WC04601) / total assets (WC02999).

PRPLEQ_SALES Property-plants-equipment to sales ratio

Property, plants, and equipment expenditures (WC02501) / sales (WC01001).

4.4.3 Descriptive statistics

Table 32 shows the descriptive statistics for the variables used in the current study.

Panel A refers to the continuous variables used in the main analysis and further

analyses, whereas Panel B illustrates descriptive statistics for categorical variables. The

results indicate the variation in firm value for the sample firms when measured by

38

Except these three new variables: CASH_ASSETS, CAPEX_ASSETS, and PRPLEQ_SALES, other

variables are used in the previous study. Thus, more details about measurement and econometric

considerations with regard to these variables can be found in chapter three: sections 3.5.3 and 3.6.1.


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Tobin’s Q (market-to-book ratio) after three months. Tobin’s Q ratio (market-to-book

ratio) three months after the year end ranges from a minimum of 0.54 (0.09) to a

maximum of 34.00 (160.46) with a mean of 2.014 (5.103). The mean and median of TQ

(MB) generally suggest that sample firms are over-valued by investors taking into

account the book value of assets (equity).

Regarding the main explanatory variables, KPI quantity scores (QNTKSEC) is

relatively small with a median of 6 KPIs. The majority of these KPIS are financial

KPIS with median of 5 KPIs.

With regard to KPI reporting quality, a low level of KPI reporting quality scores

(QLTKSEC) is observed with a mean of 0.36. Apparently, quality scores are mainly

derived by financial KPI quality scores (QLFKS). The mean of QLFKS is 0.35,

whereas a lower level of quality scores is shown for non-financial KPIs (0.27).

With respect to the main CG variables attributes, executive directors’ compensation

varies from £164,960 to £13,000,000, while it varies from £24,060 to £315,480 for non-

executive directors. The median percentage of non-executive directors is 0.625 from 9

directors (the median of board size). This indicates that non-executive directors

generally represent the majority of the board. The board meetings as a proxy of board

activity show that the meetings median is 8 times per year. The audit committee size

median is 4 directors; on average this committee has 4 meetings per year. The average

of directors’ share interests in ordinary shares is 0.05%. Finally, the major shareholders

hold an average stake of 38.9 % in the firms represented in the sample.

With regard to firm characteristics, the natural logarithm of market capitalisation for

sample firms varies from minimum of 8.00 (£17,000,000) to a maximum of 11.02

(£130 billion) with standard deviation of 0.69 (£18 billion). The mean profitability of


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these firms as measured by ROE is around 0.08. The leverage ratio indicates that firms

in the sample are not highly leveraged with a mean debt to total assets of 0.34. The

figures show that these firms vary widely with regard to their dividend yield ratios

which range from 0.0 to 0.219. Similarly, a wide variation is observed with respect to

the ratios that are introduced to control for financial performance that affects firm

growth. The variation in these ratios reflects the fact that firms vary in their ability to

grow in the future. Firms vary in terms of generating cash from their assets. This affects

their ability to meet their commitments, as well as their ability to invest in new projects

in the future. Similarly, it is shown that firms also vary in their capital expenditure

which also affects their future performance, and hence their value. This variation in

these ratios between the sample firms is expected, as the sample is drawn from FTSE

350 firms. Hence, it is essential to control for the effects of this variation in order to

ensure the robustness of the results.

Panel C shows the descriptive statistics for the categorical variables. It indicates that

most of the firms included in the sample (89.9%) are traded on foreign financial

markets. Similarly, it is noted that the majority of the sample firms (95.1%) make a

distinction between chairman and CEO positions.

Table 32 Descriptive statistics for study variables

Panel (A) Descriptive statistics for continuous variables


TQ+3 34.00 0.54 2.01 1.58 2.35 485

MB+3 160.46 0.09 5.10 2.67 14.62 485

QNTKSEC 24.00 0.00 7.53 6.00 5.10 485

QLTKSEC 0.688 0.00 0.36 0.38 0.17 485

EXCOMP 13,000 164.960 1,700 1,100 2,000 485

NOEXCOMP 315,480 24,060 77,012 65,000 43,726 485

BORSIZE 16.00 5.00 9.38 9.00 2.45 485

BORCOMP 0.86 0.33 0.62 0.63 0.12 485

BORMEET 17.00 4.00 8.59 8.00 2.50 485

ACSIZE 6.00 2.00 3.64 4.00 0.87 485


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ACMEET 8.00 1.00 4.00 4.00 1.25 485

MANGOWN 0.53 0.00 0.05 0.00 0.11 485

MAJORSHAR 0.77 0.04 0.38 0.39 0.17 485

SIZE 11.02 8.00 9.21 9.08 0.69 485

PROFITAB 0.52 -0.173 0.08 0.07 0.08 485

LEVERAGE 0.990 0.00 0.34 0.32 0.237 485

DIVYIELD 0.219 0.00 0.029 0.024 0.03 485

CASH_ASSETS 50.55 0.06 8.51 5.86 8.58 485

CAPEX_ASSETS 27.04 0.00 5.09 3.68 4.85 485

PRPLEQ_SALES 516.30 0.89 57.72 21.86 90.24 485

Variables used in further analyses:

QNFKS 19.00 0.00 5.37 5.00 3.48 485

QNNFKSEC 15.00 0.00 2.18 1.00 2.94 485

QNNFKREP 16.00 0.00 2.90 2.00 3.43 485

QNTKREP 24.00 0.00 8.24 7.00 5.43 485

QLFKS 0.69 0.00 0.35 0.38 0.17 485

QLNFKSEC 0.79 0.00 0.27 0.29 0.25 485

QLNFKREP 0.82 0.00 0.31 0.33 0.25 485

QLTKREP 0.67 0.00 0.37 0.39 0.17 485

Panel (B) Descriptive statistics for the categorical variables

Variable Proportion

CROSSLIST: Proportion of firms whom shares are traded in foreign financial markets.

89.9%

ROLEDUAL: Proportion of directors who are the chairmen and the CEO for a company at the same time.

4.9 %

Panel (A) displays descriptive statistics for continuous variables: Dependent variables: : TQ +3: TobinsQ after three months from the financial year end respectively, MB+3: Market-to-book ratio after three months from the financial year end; Explanatory

variables: QNTKSEC: the quantity of financial and non-financial KPIs disclosed in the KPI section; QLTKSEC: the aggregated quality of financial and non-financial KPIs that disclosed in KPI section; EXCO MP: Executive compensation (in thousands); NO EXCO MP: Non-executive compensation; BO RSIZE: Board size; BO RCO MP: Board composition; BO RMEET: Board meetings; ACSIZE: Audit committee size; ACMEET: Audit committee meetings; MANGO WN: Managerial ownership;

MAJO RSHAR: Major shareholding; SIZE: Firm size; PRO FITAB: Profitability; LEVERAGE: Leverage; CASH_ASSETS : Cash to total assets ratio; CAPEX_ASSETS: capital expenditures to assets ratio; PRPLEQ_SALES: property-plants-equipment to sales ratio. Variables used in further analyses : QNFKS: the quantity of financial KPIs disclosed in the KPI section; QLFKS: the

aggregated quality score of financial KPIs disclosed in the KPI section; QNNFKSEC: the quantity of non-financial KPIs disclosed in the KPI section; QLNFKSEC: the aggregated quality score of non-financial KPIs disclosed in the KPI section; Q NNFKREP: the quantity of non-financial KPIs disclosed in the whole report; QLNFKREP: the aggregated quality score of non-financial KPIs disclosed in the whole report; ; Q NTKREP: quantity of financial and non-financial KPIs disclosed in the whole report;

Q LTKREP: the aggregated quality score of financial and non-financial KPIs disclosed in the whole report. Panel (B) Descriptive statistics categorical variables: CROSSLIST: Cross listing; RO LEDUAL: Role duality (all variables are defined in Table 32).

As mentioned earlier, multicollinearity relationships among independent variables

could affect the reliability of the results. The Pearson correlation matrix is illustrated in

Table 33. It indicates that associations among the explanatory variables are below 0.80

as a threshold (Gujarati, 2003). As mentioned earlier, the relatively high correlation

between both KPI reporting quantity proxies and KPI reporting quality proxies is a

good motivation to test whether or not each of them has the same impact on firm value.


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The highly significant associations between firm value proxies represent a good

justification for using them for the purpose of checking the robustness of the results.

The correlation results also show a negative but not statistically significant association

between firm value (measured by Tobin’s Q) and KPI reporting quantity (QNTKSEC).

However, the association between firm value (measured by Tobin’s Q) and quantity of

financial KPIs (QNFKS) is negative and statistically significant. The correlation matrix

also shows that KPI reporting quality (QLTKSEC) is not associated with firm value.

However, it is obvious that there is a negative correlation between firm value (measured

by Tobin’s Q) and the quality of non financial KPI reporting quality (QLNFKSEC).

This relationship does not hold when using the market-to-book ratio as a proxy of firm

value. Thus, it might indicate that the relationship is weak, or it needs to be proved by

further empirical analysis.


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Table 33 Pearson correlation matrix

*Significance at the 5% level or above. All variables are defined in Table 32.

VARIABLE TQ+3 MB+3 QNTKSEC QLTKSEC EXCOMP NOEXCOMP BORSIZE BORCOMP BORMEET ACSIZE ACMEET MANGOWN MAJORSHAR SIZE PROFITAB LEVERAGE DIVYIELD CASH_ASSETS CAPEX_ASSETS PRPLEQ_SALES QNFKS QNNFKSECQNNFKREPQNTKREP QLFKS QLNFKSEC QLNFKREP QLTKREP

TQ+3 1

MB+3 0.8522* 1

QNTKSEC -0.1245* -0.017 1

QLTKSEC -0.0541 0.021 0.7645* 1

EXCOMP 0.1425* 0.2346* 0.2656* 0.3233* 1

NOEXCOMP 0.0855 0.2164* 0.2769* 0.2866* 0.5655* 1

BORSIZE -0.0844 -0.0125 0.2830* 0.2790* 0.4747* 0.3240* 1

BORCOMP 0.0296 0.1040* 0.2050* 0.2975* 0.5487* 0.2952* 0.1822* 1

BORMEET -0.1432* -0.0913* -0.076 -0.0176 -0.1055* -0.0827 -0.1387* -0.0098 1

ACSIZE -0.0021 0.0627 0.1188* 0.2542* 0.4080* 0.2438* 0.5208* 0.3606* -0.0211 1

ACMEET 0.0282 0.0667 0.1641* 0.2763* 0.3307* 0.2300* 0.2892* 0.3778* 0.2168* 0.3483* 1

MANGOWN -0.0048 -0.0766 -0.1146* -0.0958* -0.3139* -0.2271* -0.1693* -0.1892* -0.1177* -0.2416* -0.1472* 1

MAJORSHAR 0.0663 -0.0773 -0.1669* -0.1323* -0.4077* -0.3377* -0.3992* -0.0559 -0.0647 -0.2867* -0.1587* 0.3728* 1

SIZE 0.0759 0.1714* 0.2827* 0.2827* 0.7749* 0.5562* 0.6956* 0.4310* -0.1532* 0.4426* 0.3727* -0.2488* -0.4874* 1

PROFITAB 0.3988* 0.3309* -0.0408 0.0041 0.0817 -0.0255 -0.0495 0.0679 -0.0671 -0.0596 -0.0597 0.0926* 0.0234 0.0732 1

LEVERAGE 0.0091 0.1919* 0.1073* 0.022 0.1671* 0.2469* 0.0964* 0.1250* 0.1787* 0.0417 0.1703* -0.2808* -0.1613* 0.1475* -0.0659 1

DIVYIELD -0.1412* -0.0769 0.1205* 0.0702 0.0403 0.1511* 0.0638 0.0188 0.1437* 0.047 0.0709 -0.1269* -0.1682* -0.0046 0.0436 0.3175* 1

CASH_ASSETS 0.3094* 0.1631* -0.0814 -0.0591 -0.1562* -0.1549* -0.2510* -0.0266 -0.1206* -0.1626* -0.1511* 0.2239* 0.2896* -0.2519* 0.3221* -0.2038* -0.2180* 1

CAPEX_ASSETS -0.0298 -0.0742 -0.1357* -0.1527* -0.0562 0.0388 -0.0047 -0.1200* -0.0792 -0.1093* -0.0826 0.057 -0.0203 0.0245 0.0523 -0.0045 -0.0125 -0.0312 1

PRPLEQ_SALES -0.2283* -0.2615* -0.001 -0.0866 -0.0755 -0.0366 0.1024* 0.0369 -0.0501 -0.0409 0.0414 -0.0288 0.1272* 0.0146 -0.1724* 0.1239* -0.053 -0.0448 0.2887* 1

QNFKS -0.1570* -0.0609 0.8915* 0.7041* 0.2721* 0.2146* 0.2452* 0.2164* -0.0597 0.1225* 0.1742* -0.0949* -0.1496* 0.2452* -0.08 0.0424 0.0769 -0.1300* -0.1759* -0.0781 1

QNNFKSEC -0.078 0.0136 0.6945* 0.4302* 0.1254* 0.2455* 0.2423* 0.0846 -0.1020* 0.0604 0.0621 -0.1014* -0.1343* 0.2274* -0.0221 0.1430* 0.1470* -0.0121 0.0408 0.1409* 0.3399* 1

QNNFKREP -0.0812 0.0254 0.6321* 0.4659* 0.2357* 0.3160* 0.2344* 0.1706* -0.0496 0.1198* 0.1289* -0.1364* -0.2634* 0.2935* -0.0299 0.1169* 0.1188* -0.012 -0.0248 0.0678 0.3575* 0.7972* 1

QNTKREP -0.1289* -0.0128 0.9499* 0.7658* 0.3127* 0.3111* 0.2829* 0.2366* -0.0491 0.1493* 0.1937* -0.1324* -0.2348* 0.3104* -0.0474 0.0907* 0.1227* -0.0792 -0.1585* -0.0281 0.8655* 0.6192* 0.7458* 1

QLFKS -0.0894* -0.005 0.7328* 0.9190* 0.2942* 0.2419* 0.2787* 0.2815* -0.0371 0.2212* 0.2675* -0.0586 -0.1248* 0.2787* -0.0252 -0.0141 0.0682 -0.0968* -0.1318* -0.0758 0.7840* 0.3270* 0.3820* 0.7410* 1

QLNFKSEC -0.1033* -0.0252 0.5963* 0.5292* 0.2013* 0.2266* 0.2396* 0.1647* -0.087 0.1503* 0.1222* -0.1481* -0.1201* 0.2083* -0.0299 0.0628 0.1070* -0.0101 0.0541 0.0852 0.3545* 0.8348* 0.6443* 0.5286* 0.4220* 1

QLNFKREP -0.1127* -0.012 0.5697* 0.5494* 0.2937* 0.2863* 0.2241* 0.2546* -0.0457 0.1808* 0.1789* -0.1764* -0.2167* 0.2554* -0.0309 0.0596 0.0856 -0.0075 0.0015 0.036 0.3835* 0.6840* 0.8160* 0.6407* 0.4531* 0.8354* 1

QLTKREP -0.071 0.0156 0.7291* 0.9614* 0.3380* 0.2865* 0.2641* 0.3110* 0.0073 0.2515* 0.2865* -0.1004* -0.1508* 0.2682* 0.0004 0.0085 0.0958* -0.0553 -0.1575* -0.0875 0.6740* 0.4040* 0.4944* 0.7701* 0.8838* 0.5052* 0.5959* 1


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4.5 The main analyses

The main analyses provide evidence of the effect of financial and non-financial KPI

reporting upon firm valuation. The study achieves this aims by empirically testing the

hypotheses developed in section 4.3. This section provides the empirical results with

regard to KPIs reported in the KPI section in 4.5.1. Moreover, section 4.5.2 provides the

results with respect to the separate influence of financial and non-financial KPIs


4.5.1 The association between firm value and KPI reporting

The current study follows all the econometric procedures that have been applied in the

previous chapter, in order to ensure a high degree of confidence in the empirical

results.39 Consequently, the study follows Aggarwal et al. (2009) by winsorising all data

at 1% and 99% in order to deal with severe outliers and prevent their effect on the

results. Additionally, dependent variables are transformed - by undertaking logs of the

original values of the firm value ratios - in order to have the fewest possible outliers,

and to bring their distributions more closely to normality. Furthermore, the study

applies clustering by both firm and year, in order to address any unobserved cross

sectional and time series dependence within the panel data set (Petersen, 2009; Gow et

al., 2010). Moreover, the analyses include industry dummies to control for the industry

effect. Finally, multicollinearity among explanatory variables is checked using the

variance inflation factor (VIF). A correlation between independent variables is accepted

as long as VIF is smaller than 10 as a threshold (Gujarati, 2003; Acock, 2008).

However, to avoid any concerns regarding hidden correlations that might exist between

CG variables, these variables are grouped into groups: executive and non-executive

39

For more details about econometric considerations, see chapter three: section 3.6.1.


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directors' compensation, board characteristics, audit committee characteristics, and

ownership structure. Then, these groups are included separately - together with KPI

reporting variables and control variables - in different models (from model (1) to model

(5)) beside the general model which includes all variables (model 6). 40

Table 34 Firm value (TQ) & KPI reporting quantity in KPI section

Variable Mo1 Mo2 Mo3 Mo4 Mo5 Mo6

QNTKSEC -0.021*

0.011

-0.021*

0.011

-0.016 0.011

-0.02 0.013

-0.02 0.012

-0.017* 0.01

EXCOMP 0.058

0.061

0.051

0.059

NOEXCOMP 0.048 0.073

0.063 0.056

BORSIZE -0.011

0.008

-0.015**

0.007

BORCOMP -0.039 0.126

-0.214 0.132

BORMEET -0.007

0.008

-0.009

0.007

ROLEDUAL 0.148**

0.047

0.168**

0.059

ACSIZE 0.013

0.017

0.025

0.019

ACMEET 0.009 0.018

0.014 0.013

MANGOWN -0.139

0.181

-0.211

0.146


0.122 0.113

SIZE 0.045 0.028

0.064**

0.022

0.101***

0.026

0.058**

0.024

0.081**

0.026

0.071**

0.032

PROFITAB 0.891**

0.297

0.909**

0.309

0.851**

0.303

0.931**

0.283

0.896**

0.302

0.904**

0.282

LEVERAGE 0.071 0.077

0.067 0.083

0.075 0.059

0.069 0.068

0.071 0.077

0.054 0.063

CROSSLIST -0.003

0.041

-0.008

0.043

-0.006

0.044

0.001

0.047

-0.005

0.042

0.008

0.043

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003

0.003

0.003

0.003

0.002

0.003

0.003

0.003

0.003

0.003

0.003

0.003

PRPLEQ_SALES 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

40

The researcher thanks the external examiner Dr Basil Al-Najjar for this suggestion.


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0 0 0 0 0 0

Constant -0.64** 0.253

-0.69** 0.332

-0.634** 0.212

-0.51** 0.197

-0.66** 0.271

-1.02*** 0.251

F 11.9*** 11.8*** 11.4*** 11.2*** 11.4*** 9.8***

Adj R-squared 0.276 0.274 0.301 0.276 0.279 0.321

Mean VIF 2.05 1.89 1.91 1.85 1.87 2.16

Max VIF 3.89 3.9 3.89 3.91 4.09 5.53

N 485 485 485 485 485 485


Dependent variable: TQ +3: Tobins Q three months after the year end. Explanatory variables: QNTKSEC: the total number of financial and non-financial KPIs disclosed in the KPI section in addition to executives’ compensation in Mo1; non-executives’ compensation in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables

in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.

Table 34 shows the association between firm value (measured by Tobin’s Q) and the

aggregated quantity (QNTKSEC) scores for KPIs reported in the KPI section.

Regression models are significant at the 1% level, indicating that, on average, the

proposed models can explain about 27.4% - 32.1% of the total variation in Tobin’s Q.

Moreover, VIF values indicate that there are no concerns regarding multicollinearity

among the explanatory variables.

The results in the general model (model 6) show that the quantity of KPIs reported in

the KPI section (QNTKSEC) has a negative and statistically significant relationship

with Tobin’s Q at a level of 10%. This result holds only in model (1) and model (2) that

include executives’ compensation and non-executives’ compensation respectively. This

finding suggests that there is a weak negative association between firm value and the

amount of KPIs disclosed in the KPI section. Consequently, H1a - which expects an

association between KPI reporting quantity and firm value - is partially confirmed.

With respect to the negative effect of QNTKSEC on firm value, this finding adds to the

contradictory evidence on the relationship between accounting disclosure and firm

value. This result is inconsistent with the findings of some studies that are based on


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agency theory framework (e.g. Healy et al., 1999). These studies suggest a positive

impact in terms of enhanced disclosure upon firm valuation, thanks to the reduction in

information asymmetry between managers and shareholders. The negative effect of

QNTKSEC can be explained from different angles. First, consistent with Chung et al.’s

(2012) assertion that extra information could have a negative effect on firm value, the

excessive KPIs disclosed cause extra noise from the investors’ point of view, which

negatively affects their valuation of the firm. Second, the negative effect on firm value

could be driven by the content of the KPIs disclosed, and how it is perceived by

investors. There is a possibility that KPI information itself might raise concerns about a

firm’s performance which might lead investors to lower their valuation. In terms of this

proposition, and in accordance with the efficient market hypothesis, the more KPIs

disclosed will be reflected inversely on share price, and in turn, firm value will go

down. In contrast, KPI information could offer positive news for investors, but this

news might be less positive than their own expectations, or might make them suspicious

because it may be very different from the information gained from other sources rather

than the annual reports. Accordingly, the greater the amount of KPIs disclosed, the

more there may be a drop in firm value. Third, investors might misinterpret this practice

on the part of firms to disclose more KPI. They might perceive providing enhanced

disclosure as a way of misleading them about the actual firm performance. Investors

might also consider that a company’s rivals would benefit from this excessive critical

information, which could have a negative effect on their expectations about the firm

performance, and hence lower their valuation (Hassan et al., 2009).

However, this finding is important for UK firms. It indicates that firms have to be aware

that more KPI disclosure might have an adverse impact on their value. Therefore,


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companies have to study carefully the cost-benefit trade-off before increasing the

number of KPIs disclosed.

Regarding board characteristics variables, Table 34 shows that only board size

(BORSIZE) and role duality (ROLEDUAL) has a statically significant relationship with

firm value. The coefficient on BORSIZE is significantly negatively related to Tobin’s Q

at the level of 5% in model (6). However, this result becomes insignificant in model (3)

that focuses on the relationship between QNTKSEC as well as board characteristics on

the one hand, and firm value on the other. Thus, H4, which expects an association

between board size and firm value, is partially confirmed. This result is consistent with

several studies (e.g. Goodstein et al., 1994; Yermack, 1996; Haniffa and Hudaib, 2006;

Ujunwa, 2012). It is in line with the interpretation that big boards are not efficient

because of the free-rider problem (Ujunwa, 2012). In contrast, ROLEDUAL is

significantly positively associated with firm value at a level of 5% in models (3) and

(6). Thus, H7, which expects a significant relationship between role duality and firm

value, is confirmed. This finding can be explained by signalling theory rather than by

agency theory. CEO duality seems to be perceived by investors as a signal of effective

control and leadership. They might consider that the CEO leads the firm to achieve a

better performance due to the use of his technical knowledge.

On the other hand, the results presented in Table 34 suggest that better governance does

not lead to a higher firm valuation. Therefore, the findings do not lead us to accept the

hypotheses related to directors’ compensation, other board characteristics, audit

committee characteristics, and ownership structure.

The weak effect of CG attributes on firm value is documented in many studies (Klein,

1998; Laing and Weir, 1999; Vafeas and Theodorou, 1998; Weir et al., 2002). This


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finding agrees with a number of UK studies that question the need to impose certain CG

structures on UK firms in order to increase shareholders’ wealth (Laing and Weir, 1999;

Weir et al., 2002).

Meanwhile, it is apparent that investors pay more attention to other aspects in order to

shape their perception of firms. The results emphasise the importance of firm

characteristics in firm valuation. In particular, the results indicate that the coefficients of

size (SIZE), profitability (PROFITAB), and cash to assets (CASH_ASSETS) ratios are

statistically significant and positively related to firm value.

These results are in line with previous studies which found a positive and significant

relationship between firm value and SIZE (e.g. Hassan et al., 2009), PROFITAB (e.g.

Setia-Atmaja, 2009), and CASH_ASSETS (e.g. Aggarwal, 2009). These results are in

line with the view that firm characteristics could play a role as substitutes of board

monitoring mechanisms. For instance, large firms are subjected to more pressure and

intervention from a range of different stakeholders (e.g. shareholders, politicians, fund

suppliers, financial analysts). Therefore, one can expect that the agency problem would

be mitigated as managers of large firms are better monitored if compared with those of

small ones.

Table 35 Firm value (TQ) & KPI reporting quality in the KPI section

Variable Mo1 Mo2 Mo3 Mo4 Mo5 Mo6

QLTKSEC -0.043

0.054

-0.038

0.058

0.008

0.066

-0.046

0.065

-0.037

0.062

-0.021

0.059

EXCOMP 0.056 0.06

0.048 0.057

NOEXCOMP 0.035

0.078

0.052

0.058

BORSIZE -0.013 0.008

-0.016**

0.008

BORCOMP -0.061

0.129

-0.228*

0.133


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BORMEET -0.006

0.008

-0.008

0.007

ROLEDUAL 0.156**

0.048

0.170**

0.058

ACSIZE 0.015

0.017

0.028

0.02

ACMEET 0.009 0.019

0.013 0.013

MANGOWN -0.14 0.182

-0.211 0.145


0.123 0.116

SIZE 0.041 0.027

0.061**

0.022

0.099***

0.026

0.052**

0.025

0.076**

0.027

0.073**

0.032

PROFITAB 0.916**

0.286

0.932**

0.3

0.877**

0.295

0.955***

0.273

0.920**

0.293

0.925***

0.278


0.073 0.085

0.08 0.059

0.072 0.068

0.074 0.077

0.059 0.062

CROSSLIST -0.007

0.04

-0.012

0.042

-0.01

0.043

-0.003

0.046

-0.009

0.041

0.004

0.044

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003

0.003

0.003

0.003

0.003

0.003

0.004

0.003

0.003

0.003

0.003

0.003

PRPLEQ_SALES 0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

Constant -0.61**

0.262

-0.630*

0.358

-0.641**

0.217

-0.486**

0.202

-0.64**

0.271

-0.99***

0.261

F 11.5*** 11.4*** 11.2*** 10.9*** 11.1*** 9.6***

Adj R-squared 0.269 0.267 0.297 0.27 0.273 0.316

Mean VIF 2.06 1.89 1.91 1.85 1.88 2.16

Max VIF 3.9 3.9 3.89 3.90 4.09 5.56

N 485 485 485 485 485 485


Dependent variable: TQ +3: TobinsQ three months after the year end. Explanatory variables: Q LTKSEC is the aggregated

quality score of financial and non-financial KPIs disclosed in the KPI section in addition to executives’ compensation in Mo1; non-executives’ compensation in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; O wnership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time

clustering.

With respect to KPI reporting quality; Table 35 shows the association between firm

values measured by Tobin’s Q and the aggregated quality of KPIs reported in the KPI

section (QLTKSEC). Regression models are significant at the 1% level, indicating that,


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on average, the proposed models can explain about 26.7% - 32.1% of the total variation

in Tobin’s Q. Moreover, VIF values indicate that there are no concerns regarding

multicollinearity among the explanatory variables.

All models report that there is no significant relationship between QLTKSEC and firm

value. Hence, the hypothesis H1b which predicts a significant association between

those variables cannot be accepted. This result is not in line with several studies which

suggest that disclosure quality is value relevant to market participants (e.g. Healy et al.,

1999; Baek et al., 2004). However, the current study uses different measures of

disclosure quality. This result can be explained by the low level of KPI reporting by UK

firms in general. One can argue that investors could not perceive the differences

between these companies in terms of KPI reporting quality. Therefore, the effect of KPI

reporting quality on firm value could not be observed.

Despite the above findings with regard to the weak negative association between KPI

reporting quantity and firm value, it can be concluded that the results do not provide

strong evidence of the KPI reporting effect on firm value. Thus, the main hypothesis of

the study (H1) cannot be accepted. Accordingly, Q3 has been answered. Furthermore,

the results indicate - to some extent - that QNTKSEC and QLTKSEC may have a

different impact on firm value. Taking into consideration chapter three’s findings which

indicate that neither of them is derived from the same factors, it can be argued that

quantity of disclosure should not be used as a proxy for its quality in accounting studies.

Thus, research question (Q4) is answered.

Indeed, the findings presented -in Table 34 and Table 35- suggest that companies might

prevent the potential decline in their value by controlling the number of KPIs disclosed

in the KPI section. On the other hand, the insignificant association between the quality


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of KPI reporting and firm value will not motivate these companies to increase that

quality by following ASB (2006) guidance. However, it is recommended that they

consider the quality of disclosure together with its quantity to avoid the noise caused by

excessive information (Chung et al., 2012). Therefore, this finding is important for

regulators, in that they should reflect on ways to encourage more firms to be more

compliant.

Looking at the impact of board characteristics, Table 35 indicates that the positive and

significant impact of ROLEDUAL remains unchanged (at the 5% level in models (3)

and (6)). Similarly, the BORSIZE effect remains negative and significant, but at the 5%

level in the general model. Yet, it is shown that board composition (BORCOMP) has a

limited negative relationship with firm value at the level of 10%, based upon the results

obtained from model (6).

This result suggests that NEDs’ dominance is perceived as a signal of a non-efficient

board. Investors consider that firm performance might be influenced by NEDs’ lack of

experience. Arguably, this result indicates that investors are satisfied with a smaller

percentage of NEDs on the board. They may rely on alternative mechanisms to mitigate

the agency problem. This finding is in line with previous studies which showed that CG

can be achieved by alternative mechanisms that are adapted to firms’ own

characteristics and the surrounding environment (Vafeas and Theodorou, 1998; Weir et

al., 2002). Likewise in terms of the results reported for KPI quantity regressions, the

findings do not support the hypotheses related to directors’ compensation, other board

characteristics, audit committee characteristics, or ownership structure.

Finally, the results remain unchanged with respect to the association between firm

characteristics and firm value. In particular, the results indicate that the coefficients of


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size (SIZE), profitability (PROFITAB), and the cash to assets (CASH_ASSETS) ratios

are statistically significant and positively related to firm value.

It is worth mentioning that the models in Table 34 and Table 35 are re-estimated using

Tobin’s Q after six months from the year end, as well as market-to-book ratio (after

three and six months from the year end) as measures for firm value. It is found that the

results are not substantially different from those reported above.41

4.5.2 The association between firm value and reporting on KPI subcategories

This section reports the empirical findings with regard to the influence of reporting on

KPI subcategories. Section 4.5.2.1 presents the results with regard to financial KPI

reporting, while section 4.5.2.2 shows the results with regard to reporting on non-

financial KPIs disclosed in the KPI section. These analyses provide a clear picture with

regard to the value relevance of KPI reporting. They also give evidence of the accuracy

of using quantity and quality of disclosure as substitutes.

4.5.2.1 Firm value and financial KPI reporting

Table 36 and Table 37 show the influence of financial KPI reporting on firm value

measured by Tobin’s Q. Whereas, the results report that QNFKS has a negative and

statistically significant influence on firm value at a level of 5% in all models except

model (3), Table 37 indicates that financial KPI reporting quality (QLFKS) does not

have a significant association with firm value. These findings indicate that QNFKS and

QLFKS have different relationships with firm value, which raises more concerns about

using the quantity of disclosure as an alternative for its quality in accounting research

(Q4).

41

Results are not reported.


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Table 36 Firm value (TQ) & financial KPI reporting quantity

Variables Mo1 Mo2 Mo3 Mo4 Mo5 Mo6

QNFKS -0.029**

0.011

-0.03**

0.012

-0.024*

0.013

-0.028**

0.013

-0.03**

0.013

-0.03**

0.012

EXCOMP 0.063

0.062

0.056

0.059


0.061 0.056

BORSIZE -0.011 0.008

-0.02**

0.007

BORCOMP -0.031

0.126

-0.209

0.13

BORMEET -0.007 0.008

-0.009 0.008

ROLEDUAL 0.149**

0.048

0.167**

0.06

ACSIZE 0.013

0.017

0.025

0.019

ACMEET 0.01 0.018

0.016 0.012

MANGOWN -0.136 0.18

-0.205 0.145

MAJORSHAR 0.129

0.121

0.121

0.112

SIZE 0.044*

0.027

0.066**

0.021

0.102***

0.026

0.059**

0.025

0.082**

0.025

0.070**

0.032

PROFITAB 0.873**

0.283

0.893**

0.297

0.834**

0.29

0.916***

0.271

0.880**

0.289

0.887**

0.269

LEVERAGE 0.062

0.08

0.058

0.086

0.066

0.06

0.058

0.07

0.062

0.079

0.044

0.064

CROSSLIST 0 0.039

-0.005 0.042

-0.004 0.042

0.005 0.045

-0.002 0.04

0.012 0.042

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003

0.003

0.002

0.003

0.002

0.003

0.003

0.003

0.003

0.003

0.002

0.003


0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

Constant -0.654** 0.256

-0.69** 0.34

-0.639** 0.21

-0.511** 0.197

-0.67** 0.262

-1.0*** 0.246

F 12.1*** 11.9*** 11.6*** 11.5*** 11.6*** 9.9***

Adj R-squared 0.281 0.278 0.305 0.28 0.283 0.325

Mean VIF 2.05 1.89 1.9 1.85 1.87 2.16


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Max VIF 3.91 3.93 3.91 3.94 4.12 5.53

N 485 485 485 485 485 485


Dependent variable: TQ +3 are TobinsQ three months after the year end. Explanatory variables: : QNFKS: the quantity of financial KPIs disclosed in the KPI section in addition to executives’ compensation in Mo1; non-executives’ compensation in Mo2; board

characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard

errors in the second line for each variable are corrected for firm and time clustering.

Table 37 Firm value (TQ) & financial KPI reporting quality


QLFKS -0.051 0.045

-0.045 0.048

-0.008 0.056

-0.053 0.054

-0.045 0.053

-0.025 0.054

EXCOMP 0.056

0.061

0.048

0.057


0.051 0.058

BORSIZE -0.012 0.008

-0.016**

0.008

BORCOMP -0.055

0.129

-0.226*

0.136

BORMEET -0.006 0.008

-0.008 0.007

ROLEDUAL 0.154**

0.048

0.170**

0.058

ACSIZE 0.014

0.017

0.028

0.02

ACMEET 0.009 0.019

0.014 0.013

MANGOWN -0.137 0.182

-0.21 0.145

MAJORSHAR 0.13

0.125

0.122

0.115

SIZE 0.042 0.027

0.061**

0.021

0.099***

0.026

0.053**

0.025

0.077**

0.026

0.073**

0.033

PROFITAB 0.909**

0.279

0.925**

0.294

0.873**

0.291

0.948***

0.268

0.913**

0.287

0.921***

0.273

LEVERAGE 0.073

0.079

0.071

0.086

0.078

0.06

0.069

0.068

0.072

0.078

0.058

0.064

CROSSLIST -0.007 0.04

-0.011 0.042

-0.01 0.043

-0.002 0.046

-0.009 0.041

0.004 0.044

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003

0.003

0.003

0.003

0.002

0.003

0.004

0.003

0.003

0.003

0.003

0.003


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0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

Constant -0.615**

0.262

-0.629*

0.359

-0.639**

0.218

-0.487**

0.202

-0.643**

0.269

-0.983***

0.26

F 11.5*** 11.4*** 11.2*** 10.9*** 11.1*** 9.6***

Adj R-squared 0.27 0.268 0.297 0.271 0.273 0.316

Mean VIF 2.05 1.88 1.91 1.85 1.87 2.16

Max VIF 3.9 3.89 3.89 3.9 4.09 5.54

N 485 485 485 485 485 485


Dependent variable: TQ +3 is TobinsQ three months after the year end. Explanatory variables: QLFKS is the quality of financial

KPIs disclosed in the KPI section. in addition to executives’ compensation in Mo1; non-executives’ compensation in Mo2; board

characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.

The importance of this analysis is that the majority of UK companies place emphasis on

disclosing financial KPIs. Indeed, the results reveal that the findings discussed in the

previous section are greatly derived from the effect of financial KPI reporting. These

results suggest that companies can avoid decreases in their values by controlling the

number of financial KPIs disclosed.

When looking at the impact of CG variables, it is clear that the majority of the results

discussed in 4.5.1 are confirmed. In particular, board size (BORSIZE) is negatively

associated with firm value at a level of (5%) in the general model (model 6) in Table 36

and Table 37. Moreover, role duality (ROLEDUAL) has a positive and highly

significant effect on firm value. The coefficient of ROLEDUAL is significant at the 5%

level in model (3) and model (6), either in Table 36 or in Table 37.

Notably, Table 37 reports that board composition (BORCOMP) has a weak and

negative impact on firm value in model (3). This result suggests that NEDs’ dominance

is perceived as a signal of a non-efficient board. Investors may be of the opinion that

firm performance might be influenced by NEDs’ lack of experience.


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The rest of the results add to the robustness of the findings presented in 4.5.1. They also

highlight the relative importance of firm characteristics in terms of affecting its

valuation. In particular, firm size (SIZE), profitability (PROFITAB), and cash to assets

ratio (CASH_ASSETS) are positively and significantly associated with firm value.42

4.5.2.2 Firm value and non-financial KPI reporting

Table 38 and Table 39 illustrate the relationship between non-financial KPIs - disclosed

in the KPI section - and firm value (measured by Tobin’s Q). It is obvious that neither

the quantity (QNNFKSEC) nor the quality (QLNFKSEC) of non-financial KPI

reporting has a significant effect on firm value. It was expected that non-financial KPI

information would affect investors’ perceptions, as usually this information is not

clearly presented in the financial statement. Thus, this non-significant effect of non-

financial KPI reporting can be explained by companies’ focus on providing more

financial KPIs with a higher degree of reporting quality rather than on non-financial

ones.43 In fact, these results indicate that the key findings discussed in 4.5.1 are derived

from the impact of financial KPI reporting.

With respect to the impact of CG variables, Table 38 and Table 39 show that the board

size (BORSIZE), board composition (BORCOMP) and role duality (ROLEDUAL)

variables continue to show the same influence on firm value illustrated in 4.5.2.1.

Similarly, for control variables, only firm size (SIZE), profitability (PROFITAB), and

cash to assets ratio (CASH_ASSETS) have a positive and significant association with

42

It is worth mentioning that the models in Table 36 and Table 37 are re-estimated using Tobin’s Q after

six months from the year end, as well as the market-to-book ratio (three and six months from the year

end) as measures for firm value. It is found that the results are not substantially different from those

reported in 4.5.2.1. 43

For more detail, please see their statistical results at Table 33.


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firm value.44

Table 38 Firm value (TQ) and quantity of non-financial KPIs reported in the KPI

section


QNNFKSEC -0.011 0.015

-0.011 0.015

-0.007 0.014

-0.01 0.015

-0.011 0.015

-0.007 0.012

EXCOMP 0.049

0.063

0.045

0.061


0.053 0.055

BORSIZE -0.012 0.008

-0.016**

0.007

BORCOMP -0.053

0.124

-0.225*

0.13

BORMEET -0.007 0.008

-0.008 0.007

ROLEDUAL 0.153**

0.048

0.172**

0.059

ACSIZE 0.014

0.017

0.027

0.02

ACMEET 0.007 0.017

0.013 0.012

MANGOWN -0.141 0.182

-0.215 0.143

MAJORSHAR 0.129

0.124

0.122

0.115

SIZE 0.043 0.028

0.060**

0.022

0.099***

0.026

0.052**

0.025

0.075**

0.026

0.073**

0.032

PROFITAB 0.915**

0.299

0.928**

0.311

0.867**

0.306

0.951***

0.284

0.916**

0.303

0.921**

0.286

LEVERAGE 0.08

0.08

0.077

0.086

0.081

0.061

0.078

0.072

0.078

0.08

0.061

0.064


-0.01 0.043

-0.009 0.044

-0.003 0.047

-0.008 0.042

0.004 0.044

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003

0.003

0.003

0.003

0.003

0.003

0.004

0.003

0.004

0.003

0.003

0.004


0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

44

The majority of these results are confirmed when using Tobin’s Q after six months from the year end

as well as the market-to-book ratio (three and six months from the year end) as measures for firm value in

the analyses.


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Constant -0.604** 0.255

-0.634* 0.337

-0.637** 0.216

-0.490** 0.199

-0.643** 0.276

-0.982*** 0.267

F 11.5*** 11.4*** 11.3*** 10.9*** 11.1*** 9.6***

Adj R-squared 0.27 0.268 0.297 0.27 0.274 0.316

Mean VIF 2.06 1.9 1.92 1.86 1.88 2.16

Max VIF 3.9 3.9 3.9 3.91 4.1 5.52

N 485 485 485 485 485 485


Dependent variable: TQ +3: is Tobin’s Q three months after the year end. Explanatory variables: : Q NNFKSEC: the quantity of non-financial KPIs disclosed in the KPI section in addition to executives’ compensation in Mo1; non-executives’ compensation in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5; and all

explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clusterin g.

Table 39 Firm value (TQ) & quality of non-financial KPIs reported in KPI section

Mo1 Mo2 Mo3 Mo4 Mo5 Mo6

QLNFKSEC -0.053

0.042

-0.05

0.043

-0.03

0.044

-0.053

0.047

-0.053

0.044

-0.046

0.037

EXCOMP 0.06 0.06

0.051 0.059

NOEXCOMP 0.04 0.075

0.057 0.054

BORSIZE -0.012

0.008

-0.015**

0.007

BORCOMP -0.041 0.127

-0.213*

0.129

BORMEET -0.007 0.008

-0.008 0.007

ROLEDUAL 0.151**

0.048

0.169**

0.059

ACSIZE 0.015 0.017

0.028 0.02

ACMEET 0.009 0.018

0.014 0.013

MANGOWN -0.155

0.178

-0.222

0.143


0.124 0.114

SIZE 0.041 0.027

0.061**

0.022

0.09***

0.026

0.053**

0.024

0.077**

0.027

0.069**

0.03

PROFITAB 0.900**

0.299

0.917**

0.311

0.862**

0.308

0.941***

0.284

0.904**

0.302

0.911**

0.288


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0.069 0.083

0.076 0.058

0.069 0.067

0.069 0.075

0.053 0.06

CROSSLIST -0.004

0.041

-0.009

0.043

-0.008

0.044

-0.0004

0.047

-0.006

0.041

0.006

0.044

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003 0.003

0.003 0.003

0.003 0.003

0.004 0.003

0.004 0.003

0.003 0.004

PRPLEQ_SALES 0.0001

0

0.0001

0

0.0001

0

0.0001

0

0.0001

0

0.0001

0

Constant -0.638** 0.255

-0.663** 0.332

-0.638** 0.217

-0.502** 0.199

-0.654** 0.28

-1.001*** 0.269

F 11.7*** 11.6*** 11.3*** 11.1*** 11.3*** 9.7***

Adj R-squared 0.273 0.271 0.298 0.274 0.277 0.319

Mean VIF 2.05 1.88 1.91 1.84 1.87 2.16

Max VIF 3.89 3.9 3.89 3.91 4.09 5.57

N 485 485 485 485 485 485


Dependent variable: TQ +3: is TobinsQ three months after the year end. Explanatory variables: : Q LNFKSEC: the aggregated

quality of non-financial KPIs disclosed in the KPI section in addition to executives’ compensation in Mo1; non-executives’ compensation in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include

industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.

4.6 Further analyses

This section would examine whether the KPIs reported outside the KPI section could

affect the findings discussed above. Section 4.6.1 illustrates the results with regard to

reporting on non-financial KPIs disclosed in the whole report, and section 4.6.2 that

displays the results with regard to KPI reporting over the whole report after considering

KPIs disclosed outside the KPI section. These analyses provide an overall picture about

the value relevance of KPI reporting.

4.6.1 Firm value and total non-financial KPI reporting

These analyses aim at investigating whether considering non-financial KPIs disclosed

outside the KPI section could affect the findings in 4.5.2.2. Table 67 and Table 68 in


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Appendix (3) indicate that regression models are significant at the 1% level, with high

R2, and with no multicollinearity concerns. In general, the results reported in Table 67

confirm the finings discussed above with regard to the impact of non-financial KPI

reporting quantity (QNNFKREP) upon firm value. However, Table 68 shows that the

quality of non-financial KPI reporting (QLNFKREP) has a negative and statically

significant relationship with firm value in all models except in models (3) and (4).

Hence, this indicates that the impact of the quality of non-financial KPI reporting

becomes stronger when the scores of these KPIs are aggregated with those reported

outside the KPI section.

As mentioned earlier, financial performance is one of the key drivers to improving firm

valuation. Non-financial KPIs that are reported outside the KPI section are generally

related to social and environmental aspects. Therefore, this result can be explained by

investors’ negative expectations with regard to the financial consequences of those

issues. By following the ASB (2006) guidance, firms with high QLNFKREP shall

provide additional information which is not included in financial statements. This

information covers operational, social and environmental aspects, including their targets

and management commentary on these targets. In accordance with the efficient market

hypothesis, investors may reflect this information on the financial commitments in the

future, and hence they can incorporate this information in share prices, and hence lower

their valuation of these firms. Indeed, this explanation might need to be confirmed by

analysing the content of these KPI disclosures.

It is worth mentioning that the results presented in Table 67 and Table 68 indicate that,

from CG attributes, only board size (BORSIZE) and role duality (ROLEDUAL)

variables continue to show the same influence on firm value as illustrated in 4.5.2.1.


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Likewise, firm size (SIZE), profitability (PROFITAB) and cash to assets ratio

(CASH_ASSETS) from the control variables show a positive and significant

association with firm value.45

4.6.2 Firm value and total KPI reporting

These analyses aim at investigating whether considering non-financial KPIs disclosed

outside the KPI section could affect the findings of the main analyses which are

reported in section 4.5.1. In general, it observed that KPIs reported outside the KPI

section do not affect the results discussed earlier in section 4.5.1. Table 69 and Table 70

in Appendix (3) document that the results, with regard to the effect of quantity

QNTKREP and quality QLTKREP of KPI reporting on firm value, are still as same as

reported in Table 34 and Table 35. However, it seems that the effect of KPI reporting

quantity on firm value has been maximised. Hence, Table 69 illustrates that the quantity

of KPIs reported in the whole report (QNTKREP) has a negative and statistically

significant association with firm value in all models except model (3). This association

becomes significant at the level of 5% - as reported in model (1) and (2) - instead of the

level of 10% which is reported in Table 34 .

On the other hand, Table 70 shows that the quality of KPIs reported in the whole report

(QLTKREP) does not have a significant association with firm value. This finding is the

same as the finding reported in Table 35 without considering KPIs reported outside the

KPI section. Arguably, these findings are important as they show different relationships

between KPI reporting quantity and KPI reporting quality on the one hand, and firm

45

The models in Table 67 and Table 68 are re-estimated using Tobin’s Q after six months from the year

end as well as market-to-book ratio (three and six months from the year end) as measures for firm value.

It is found that the results are not substantially different from those reported in 4.6.1.


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value on the other. These findings could contribute to answering Q4. Hence, it indicates

that reporting quantity and its quality should not be used interchangeably.

Similarly, when looking at the impact of CG and other variables upon firm value, it is

clear that the results have not been changed at all from those reported in Table 34 and

Table 35.

To conclude with regard to the results of the main and further analyses conducted in

this chapter, it can be claimed that firm value measured by Tobin’s Q is negatively

associated with the quantity of KPI reporting. Panel (A) of Table 40 indicates that this

finding becomes more significant after including KPIs reported outside the KPI section

in the analyses. Panel (B) shows that, whereas the effect of KPI reporting quality on

firm value is not significant, this effect is statistically significant for the quality of non-

financial KPI reporting if KPIs reported outside the KPI section are considered in the

tests. Finally, one can argue that the relationship between KPI reporting and firm value

seems to be derived from the effect of financial KPI reporting.

Table 40 Firm value (TQ) and quantity and quality of KPI reporting Panel (A) Firm value (TQ) & quantity of KPI reporting


QNFKS (-)** (-)** (-)* (-)** (-)** (-)**

QNNFKSEC

QNNFKREP

QNTKSEC (-)*

(-)*

(-)*

QNTKREP (-)**

(-)** (-)* (-)* (-)*

***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. (+) positive relationship; ( -) Negative

relationship. QNFKS is the number of financial KPIs disclosed in the KPI section; Q NNFKSEC is the number of non-financial KPIs disclosed in the KPI section; QNNFKREP is the number of non financial KPIs disclosed in the whole report; Q NTKSEC is the total number of financial and non-financial KPIs disclosed in the KPI section. QNTKREP is the total number of financial and non-financial KPIs disclosed in the whole report in addition to executives’ compensation in Mo1; non-executives’ compensation

in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31.

Panel (B) Firm value (TQ) and quality of KPI reporting


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QLFKS

QLNFKSEC

QLNFKREP (-)** (-)* (-)* (-)*

QLTKSEC

QLTKREP

***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. (+) positive relationship; (-) Negative relationship. QLFKS is the quality score of financial KPIs disclosed in the KPI section; Q LNFKSEC the quality score of non- financial KPIs disclosed in the KPI section; QLNFKREP the quality score of non-financial KPIs disclosed in the whole report;

Q LTKSEC the aggregated quality score of financial and non-financial KPIs disclosed in the KPI section. Q LTKREP the aggregated quality score of financial and non-financial KPIs disclosed in the whole report in addition to executives’ compensation

in Mo1; non-executives’ compensation in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; O wnership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. .

4.7 Conclusion

Generally, there is a limited empirical literature on corporate disclosure impact on firm

value. Furthermore, most of the previous studies focused only on disclosure quantity

rather than its quality. KPI reporting offers good tools for evaluating the current and

future performance of a firm. Thus, KPI disclosure would mitigate the information

asymmetry problem, so that it might have effects on firm valuation. The current study

provides answers to Q3 and Q4 of the research questions; it investigates KPI reporting

(quantity and quality) effect on firm valuation in the UK (Q3). The analyses findings

contribute also in providing some evidence with regard to using quantity of disclosure

as a proxy for quality in accounting studies (Q4).

The study mainly draws upon agency theory to explain how KPI reporting could affect

firm value. It is also in line with the view that investors would incorporate KPI

information in their share prices’ valuation in accordance with the efficient market

hypothesis. The study sample is identified as 103 firms of the FTSE 350 non-financial

UK firms over a five year period (2006-2010). Panel data regressions are conducted to

test the hypotheses of the study. After controlling for firm characteristics as well as


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growth opportunities, the results show that the quantity of KPIs disclosed in the KPI

section has a negative and significant association with firm value. Moreover, the results

indicate that the quality of KPIs disclosed in the KPI section has no impact upon firm

value.

The findings indicate that investors may perceive that higher amounts of KPI disclosed

is a signal of noise caused by the management to hide some threats or problems. In

addition, KPI information disclosed might raise their concerns about firm performance,

or lead them to correct their overvaluation of share prices based on KPI disclosures.

The above findings suggest that the quantity and quality of KPI reporting have different

relationships with firm value. This evidence questions again the validity of using

quantity of disclosure as a proxy for its quality in accounting research. Therefore, it can

be argued that quantity of disclosure and its quality should not be considered as

substitutes.

The analyses are extended in order to investigate the impact of KPI subcategories’

reporting upon firm value. These analyses provide evidence of the influence of financial

KPIs disclosed on the findings gained from the main analyses. It is indicated that the

association between firm value and KPI reporting is greatly derived from the effects of

financial KPI reporting. Furthermore, the study finds a negative effect of non-financial

KPIs disclosed when considering non-financial KPIs that are disclosed outside the KPI

section. This suggests that firm value might be lowered due to investors’ negative

expectations with regard to the financial consequences of social and environmental

aspects.


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The findings of the current study agree with Hassan et al.’s (2009) assertion that the

relationship between corporate disclosure and firm value is complex and varied, based

upon disclosure type which makes it more complex to study.

It is noteworthy that those CG mechanisms proposed in the literature do not have a

significant effect on firm value. The findings with respect to CG attributes can be

explained by signalling theory rather than agency theory. These findings suggest that

clear leadership and effective control are essential factors for investors. Accordingly,

firms with smaller number of members serving on board, as well as firms chaired by

CEOs, are conveying good signals to investors that they have effective leadership.

Consequently, investors improve their valuations for these firms. Furthermore, it is

reported that there is a weak - and in some models statically significant - negative effect

of board composition on firm value. This result suggests that investors might have

concerns about NEDs’ potential lack of experience.

On the other hand, it is apparent that firm size, profitability, and cash to assets ratios

have positive and significant impact on firm value. This may indicate that investors

might place more emphasis on such attributes to act a role in board monitoring.

The study findings are important for UK firms, suggesting that investors pay more

attention to financial KPIs disclosed in the annual report than to non-financial ones. In

sum, these firms have to study carefully the cost-benefit trade off before increasing the

number of KPIs disclosed.

CHAPTER FIVE: CONCLUDING REMARKS

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Chapter 5 - Concluding remarks

5.1 Overview

Directors of UK firms are asked to analyse business performance from the point of view

of different aspects using KPIs (CA, 2006; ASB, 2006). However, these regulations in

reality allow those directors to control the number of KPIs disclosed and the reporting

quality. This situation has resulted in a variation between firms in practice. Firms could

use reporting on KPIs to affect the perceptions of different stakeholders. Moreover, it

could be anticipated that the valuation of UK firms could be influenced by the level of

KPI disclosure and/or its quality. Therefore, the present research has explored the

practices of UK firms with regard to KPI reporting. In addition, it has investigated the

potential drivers of KPI reporting in terms of quantity and quality. Finally, the research

has been extended to examine whether or not KPI reporting quantity and quality could

have an impact on UK firms’ values.

This chapter provides the concluding remarks of this thesis. The remainder of this

chapter is organised as follows: section 5.2 provides a summary of the research

questions, objectives and approach. Section 5.3 presents a summary of the key findings

of the research and discusses their implications. Section 5.4 shows the contributions and

implications of the study. Section 5.5 illustrates the limitations of this research. Section

5.6 highlights several opportunities for future research.

5.2 Summary of research questions, objectives and approach

To contribute to the literature, this research has adopted different approaches which

have provided answers to the research questions. Hence it has achieved its objectives.


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5.2.1 Research questions

This research has provided answers to the following four research questions:

Q1. What are the main features of KPI reporting in the UK?

Q2. What are the factors affecting the level of quantity and quality of KPI reporting in

the UK?

Q3. What is the impact of KPI reporting quantity and quality on firm value?

Q4. Can KPI reporting quantity be used as a proxy for KPI reporting quality?

5.2.2 Research objectives

Taking into consideration the limited literature that addresses KPI reporting (Hussainey

and Walker, 2006; Boesso and Kumar, 2007; Giunta et al., 2008; Tauringana and

Mangena, 2009), the present study has provided answers to the above research

questions by pursuing the following objectives

1. Providing a proper measure for KPI reporting quality and quantity.

2. Exploring the main features of KPI reporting in the UK.

3. Identifying the determinants of KPI reporting in terms of quantity and quality.

4. Investigating the impact of KPI reporting in terms of quantity and quality upon

firm value.

5. Examining the extent to which KPI reporting quantity can be used as a proxy for

KPI reporting quality.


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5.2.3 Research approach

The following subsections shows the approaches followed in order to provide answers

to the research questions.

5.2.3.1 Research question 1

To provide an answer to Q1, first an index has been developed to measure KPI

reporting in terms of quantity and quality in the annual reports. The quantity of KPI

disclosure has been measured by counting the number of KPIs disclosed in the annual

reports. With regard to KPI reporting quality, a review of the previous attempts to

assess disclosure quality in general has been conducted. Then, the research instrument

has been constructed based upon the ASB (2006) guidance for best practice that

enhances information quality through eight dimensions. Manual content analysis has

been used to code the text and to classify the KPIs disclosed into financial KPIs and

non-financial KPIs. The research instrument was employed to obtain the KPI reporting

quantity and quality scores for a sample of FTSE 350 non-financial UK firms. The

study sample was identified as 103 firms with 515 annual reports published between

2006 and 2010. Descriptive statistics have been used to explore the variation between

firms in KPI reporting, including its subcategories. In addition, descriptive results were

presented to show changes in KPI reporting in terms of quantity and quality among

different industries and to illustrate this variation across the period 2006-2010.


To provide an answer to Q2, the study has reviewed the relevant theories that explain

directors’ motivations with regard to controlling corporate disclosure. Consequently,

the determinants of KPI reporting in terms of quantity and quality have been proposed,


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drawing on agency theory, signalling theory, capital need theory, political need theory,

stakeholder theory and information cost theory. In addition to firm characteristics

variables, the main variables tested are directors’ compensation, board size, board

composition, board meetings, role duality, audit committee (AC) size, AC meetings,

managerial ownership, major shareholding, and the issuance of shares, bonds and loans.

In addition to the Pearson correlation matrix, panel data regressions have been

conducted to assess the significance of the association between determinants, variables

and KPI reporting quantity and quality scores. The study has employed clustering by

firm and time effects to determine any unobserved cross sectional and time series

dependence within the panel data set.


To provide an answer to Q3, the relevant literature has been reviewed. Agency theory

as well as the efficient market hypothesis is used to explain how KPI reporting could

affect firm value. Following previous studies, the study has controlled for firm

characteristics as well as growth opportunities. Additionally, the following variables

have been included in the analyses: KPI reporting quantity and quality scores, directors’

compensation, board size, board composition, board meetings, role duality, audit

committee (AC) size, AC meetings, managerial ownership, and major shareholding.

Following previous studies (e.g. Haniffa and Hudaib, 2006; Hassan et al., 2009;

Aggarwal, 2009), Tobin’s Q ratio has been used in the main and further analyses as a

measure of firm value. Moreover, tests have been re-estimated using the market-to-

book ratio as a proxy for firm value, in order to check the robustness of the results.

Panel data regressions have also been conducted to test the hypotheses of the study. The


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study has applied clustering by both firm and year in order to address firm and time

effects within the panel data set.


This thesis consists of three studies which are integrated to provide an answer to Q4.

First, the research approach is to make a distinction between disclosure quantity and its

quality. Therefore, the research instrument has been developed to measure the quantity

of KPI reporting separate from the quality of KPI disclosure. To test the reliability of

the measure, a pilot study was conducted on a sample of 10 annual reports for the year

2009-2010. Then, the measure has been used to come up with KPI reporting quantity

and quality scores. Regression results in the second study have been employed to

indicate whether each of the quantity and quality of KPI reporting is identically derived

from the same factors. Finally, the findings of the third study have used the sign and the

significance of the relationship between KPI reporting quantity and quality and firm

value, in order to examine whether the quantity and quality of KPI reporting have

different effects on firm valuation.

5.3 Research findings

This section includes a summary of the findings of the studies that were conducted to

achieve the research objectives. These findings will be linked with the key research

questions.

5.3.1 The attributes of KPI reporting in the UK (Q1)

The analyses in chapter (2) have revealed that the majority of firms introduce their KPIs

within the business review. However, it is apparent that directors take advantage of

allowing them to report on KPIs if they consider them as necessary and appropriate to


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analysing the firm’s performance, and to avoid reporting on KPIs when they consider

such disclosure against the firm’s competitive position. As a result, many companies do

not disclose KPIs at all, while, a large number of companies limit their disclosure to

providing financial KPIs.

The most popular financial KPIs disclosed by UK firms were: revenues followed by

underlying earnings per share, and free cash flow. In contrast, the most popular non-

financial KPIs disclosed were: accident incident rate, employee turnover\ retention, and

accident numbers. Despite the increasing trend in the KPI reporting quantity and quality

levels across the sample period (2006-2010), descriptive statistics have documented the

low number of non-financial KPIs disclosed, as well as the low quality of KPI reporting

in general. The number of non-financial KPIs disclosed is not enough to analyse

environmental and people aspects. The overall quality level of KPIs reported is slightly

improved if KPIs disclosed outside the KPI section are considered. Furthermore, it has

been noted that the quality of non-financial KPI reporting is usually lower than the

corresponding value for financial ones during the sample period. The study has found

that firms do not comply with most of the qualitative attributes included in the ASB

(2006) guidance for best practice with regard to KPI reporting.

The analyses have revealed that the industries with the highest quantity of KPI

disclosure do not appear to be the highest in terms of quality of KPI disclosure. While,

Utilities firms were the highest in KPI reporting quantity, the highest level of KPI

reporting quality was provided by Basic Materials industry. In contrast, Healthcare

firms showed the lowest level of KPI reporting quantity, but Oil & Gas and Technology

firms provided the lowest level of total KPI reporting quality.


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5.3.2 Factors explaining the variation in KPI reporting (Q2)

The analyses in chapter (3) have revealed that board size, board composition, non-

executives’ compensation, and firm’s plans to acquire loans have significant and

positive relationships with KPI reporting quantity as well as its quality. In contrast, role

duality has a negative influence on both of them. However, it has been observed that

other variables have different effects on the two main dimensions in terms of disclosure

(i.e. quantity and quality). Whereas, executive compensation and audit committee

meetings have a positive influence on the quality of KPI disclosures rather than its

quantity, those firms intent to issue bonds have been found to have a positive influence

on the quantity of KPI rather than on its quality. Moreover, board meetings show a

negative association only with KPI reporting quantity.

These findings are important for many reasons. First, with regard to directors’

compensation, there are a few previous studies that have examined the directors’

compensation effect on corporate disclosures (e.g. Aboody and Kasznic, 2000; Nagar et

al., 2003; Grey et al., 2012). The findings of this study add to the literature by providing

strong evidence that highly compensated directors tend to publish more information

with a higher level of quality. Therefore, it can be argued that shareholders could use

managerial remunerations to increase the quantity and quality of KPI information which

is disseminated by the directors. These findings are in line with disclosure agency and

signalling theories. Highly compensated directors tend to disclose high levels of KPI

information quantity and quality that might include their private information, and hence

mitigate information asymmetry between directors and shareholders. Accordingly, it

can be argued that highly compensated directors - especially non-executive ones - are

keen to improve KPI reporting in order to signal their competence in the employment


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market. Second, with regard to board characteristics variables, it has been found that the

larger the board size and the higher the percentage of NEDs on the board, the higher the

possibility of providing high quantity and quality KPI reporting. In contrast, combining

the CEO and the chairman roles results in a negative effect on the number of KPIs

disclosed and its quality.

These results can be interpreted in terms of the propositions of agency and signalling

theories. The results illustrate that an effective board monitoring role leads to the

disclosure of more KPIs and improved reporting quality. Arguably, the results might

also indicate that board directors - especially NEDs - have the incentive to attract

different employers through reporting on more KPIs with a higher quality. These

findings are in line with the previous literature that examines the relationship between

disclosure quantity and board size (e.g. Laksamana, 2008; Hussainey and Al-Najjar,

2011), board composition (e.g. Li et al., 2008; Wang and Hussainey, 2013), and role

duality (e.g. Haniffa and Cooke, 2002; Abdelsalam and Street, 2007).

Additionally, the study has shown that audit committee meetings have a positive

influence on the quality of KPIs. The results suggest that active ACs are of great

importance when it comes to oversight and the control of financial reporting. Therefore,

UK firms should be encouraged to follow the FRC (2012) recommendation that asks for

many AC meetings, with a minimum of three meetings per year.

The findings of the study have documented a positive and statistically significant

association between corporate tendency to issue bonds or get loans, and the number of

KPIs reported in the KPI section. These findings are interpreted as being in line with the

premises of capital need theory. Directors are driven by the need to obtain finance to


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increase the volume of KPI disclosure. Meanwhile, those directors do not show the

same concern with reporting quality.

However, the study has found no significant relationship between the plans to issue

equity and KPI reporting. This result can be interpreted in line with Lang and

Lundholm’s (2000) findings that there is a trade-off between managers’ incentives to

mitigate information asymmetry through increasing KPI quantity and quality, and the

motivation to maintain a steady level of disclosure so that they avoid any major decline

or correction in the share price after the announcement date. The results with regard to

capital need variables show that directors affect KPI reporting based upon the source of

finance.

Finally, the analyses findings have shown that KPI reporting is not associated with the

majority of firm characteristics. These results highlight the importance of CG

mechanisms as drivers of financial reporting. However, similar findings have been

provided by previous studies with respect to profitability (e.g. Mangena and Pike,

2005), liquidity (e.g. Anis et al., 2012), leverage (e.g. Ho and Wong, 2001; Abraham

and Cox, 2007), dividend yield (Naser et al., 2006), and cross listing (e.g. Oyelere et al.,

2003).

5.3.3 KPI reporting and firm value (Q3)

The analyses in chapter (4) have revealed the following. While the number of KPIs

disclosed in the KPI section has a negative and significant association with firm value,

KPI reporting quality has no statistical and significant relationship with firm value.

Notably, when considering the quantity of KPIs disclosed outside the KPI section in the

analyses, the negative association between KPI reporting quantity and firm value

becomes more significant. The analyses have indicated that the association between


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firm value and KPI reporting is largely derived from the effects of financial KPI

reporting. In contrast, neither the quantity nor the quality of non-financial KPI reporting

has a significant association with firm value.

These results are consistent with Hassan et al.’s (2009) assertion that the association

between corporate disclosure and firm value is as complex as it is varied, based upon

disclosure type. The findings with regard to KPI quantity suggest that market

participants might perceive that directors in firms with extra amounts of KPIs disclosure

are causing such noise in order to hide some potential threats or problems.

The findings could also be related to the content of the KPI information disclosed. This

content might reflect the real position of the business, which raises concerns about the

firm’s prospects. In line with the efficient market hypothesis, market participants’

incorporate this information and correct their overvaluation of share prices.

On the other hand, it has been concluded that investors could not assign higher values

for firms with a higher quality of KPI reporting. This finding might be explained by the

low level of quality scores for the majority of UK companies. Therefore, investors

could not perceive any differences between these companies in terms of disclosure

quality. Hence, these differences have not been reflected on their valuation of UK firms.

In addition, the analyses illustrated that the majority of corporate governance (CG)

mechanisms do not have a significant effect on firm value. Whereas, board size has a

significant and negative association with firm valuation, a positive and highly

significant effect of role duality on firm value is documented. These findings can be

explained by signalling theory, suggesting that firms with smaller board size, and firms


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chaired by CEOs, are conveying good signals to investors that they have an effective

type of leadership and control.

5.3.4 Quantity and quality of financial reporting (Q4)

The answers to Q1, Q2 and Q3 are integrated to give an answer to Q4. More

specifically, the analyses in chapter (2) regarding Q1 (i.e. the attributes of KPI reporting

in the UK) have indicated that industries with the highest quantity of KPI disclosure did

not appear to be the highest in terms of quality of KPI disclosure.

In addition, the analyses in chapter (3) regarding Q2 (i.e. the factors affecting KPI

reporting quantity and quality) have revealed that there is a positive correlation between

KPI reporting quantity and its quality. This suggests that the larger the quantity of KPIs

disclosed, the higher the quality of KPI reporting.

However, the empirical analyses have revealed the following. The quantity and quality

of KPI reporting are not identically influenced by the same factors. The study results

indicated in 5.3.2, question the proposition of using quantity of disclosure as a proxy for

its quality in accounting studies that examine the determinants of accounting disclosure.

Furthermore, the findings regarding Q3 (i.e. the impact of KPI reporting on firm value)

in chapter (4) facilitate answering Q4. The findings suggest that the quantity of KPI

reporting and its quality have different associations with firm value. The evidence

indicates that, whereas the quantity of KPIs has a significant and negative effect on firm

value, KPI reporting quality has no impact upon firm value. Even when quantity and

quality of KPIs reported outside the KPI section were considered in the analyses, the

above findings hold.


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These findings are important as they provide evidence suggesting that it is not

appropriate to use the quantity of disclosure as a proxy for its quality in accounting

research while investigating the value relevance of accounting information. Overall, the

present study findings are in line with the recent literature (Anis et al., 2012) suggesting

that disclosure quantity and disclosure quality should not be used as substitutes for one

another.

5.4 Contributions and implications

This section indicates how this thesis contributes to the extant literature. Then, the

implications of the present study are provided.

5.4.1 Contributions

This thesis makes a contribution to the literature by answering the four research

questions. Furthermore, the thesis could add to the methodologies applied in the

literature.

5.4.1.1 Contributions to the literature

The answer to Q1 extends the limited literature that explores KPI reporting in practice:

Giunta et al. (2008) (Italy) and Tauringana and Mangena (2009) (UK). However, the

present study is distinguished by investigating the level of quantity as well as quality of

KPI reporting for a relatively large sample of UK listed companies from different

sectors over a five year period.

The answer to Q2 contributes to the academic studies testing the role of CG

mechanisms and firm characteristics as determinants of corporate disclosure (e.g.

Forker, 1992; Cooke, 1992; Ho and Wong, 2001; Haniffa and Cooke, 2002; Ajinkya et

al., 2005; Li et al., 2008; Tauringana and Mangena, 2009; Hussainey and Al-Najjar,


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2011; Wang and Hussainey, 2013).

The answer builds on, and contributes to, the literature suggesting that the quantity and

quality of disclosure are not derived from the same factors. Furthermore, the study

highlights the importance of directors’ compensation as a driver of financial reporting.

The findings provide strong support for the proposition of agency, signalling, and

capital need theories, when testing the effect of different factors on KPI reporting in

terms of quantity and quality.

The answer to Q3 contributes to the previous literature examining the impact of

financial reporting on market participants (e.g. Hussainey and Walker, 2009; Hassan et

al., 2009; Mouselli et al., 2012). To the best of the author’s knowledge, this is the first

study which illustrates that the quantity and quality of reporting have different

relationships with firm value. Moreover, the study also builds on, and contributes to, the

literature investigating the relationship between CG mechanisms and firm value (e.g.

Klein, 1998; Dalton et al., 1999; Laporta et al., 2002; Haniffa and Hudaib, 2006;

Aggarwal et al., 2009; Ammann et al., 2011). As investors do not assign higher value to

firms with most of the CG variables, the findings are in favour of allowing UK firms to

select a CG structure which is appropriate to their own characteristics.

To provide an answer to Q4, the study has to address a number of issues. First, the call

for financial reporting quality measures which are directly derived from a proper

definition of disclosure quality (Beyer et al., 2010). Second, the call for solving the

difficulty in measuring disclosure quality, by using a comprehensive measure rather

than earning quality (Berger, 2011). The study builds on, and contributes to, the

literature focusing on the qualitative attributes of the information disclosed (e.g. Beattie

et al., 2004; Beretta and Bozzolan, 2004; Botosan, 1997; Boesso and Kumar, 2007;

Giunta et al., 2008; Beest and Braam, 2011; Anis et al., 2012). The study introduces


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reporting quality measures based on the ASB (2006) framework. Employing this

measure in accounting research would also contribute to knowledge. For instance, it

might lead to different inferences with regard to the drivers and impacts of either

narrative disclosure in general, or specific types of disclosure (e.g. risk reporting, social

responsibility reporting).

5.4.1.2 Methodological contributions

The study introduces a valid and reliable measure of disclosure quality. Hence, it

enables researchers to distinguish between disclosure quantity and quality when

exploring firms’ practices. In addition, the results of applying this measure provide

strong evidence that using the quantity of disclosure as a proxy for its quality in

accounting studies might lead to misleading inferences with regard to factors affecting

financial reporting. This might also have implications with respect to the economic

consequences of accounting disclosure.

In addition, the study employs clustering by both firm and time in order to consider any

unobserved firms and times within the data set. Compared with other methods applied

in accounting research, Gow et al. (2010) found that clustering by firm and time (CL-2)

would produce well specified tests statistics if compared with OLS standard errors,

White standard errors, Newey-West standard errors, Fama-MacBeth, Z2, as well as

robust standard errors clustered by time, firm and both.

5.4.2 Implications

The findings of this thesis should be relevant to the regulatory bodies. As the UK

Minister for Employment Relations, Consumer and Postal Affairs stated, companies

have to disclose relevant information of a high quality within their narratives. The

findings of the study inform policy makers about the relatively low level of KPI


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reporting quality in the annual report narratives (especially for firms in some sectors

such as Oil & Gas). The weak performance with regard to several attributes of KPI

reporting quality confirms the concerns regarding the role of enforcement mechanisms.

This suggests that firms might need clear guidance that indicates best practice in detail.

More specifically, this guidance should show firms how to indicate the link between

firms’ KPIs and their strategies, quantify their KPI targets, provide commentary on

these targets, and disclose any changes in KPIs.

Accordingly, the present study has revealed that there is a variation between companies

from different industries in terms of the amount of KPIs disclosed. Furthermore, it has

shown that disclosure on KPIs - especially non-financial ones - was absent in many

annual reports. Therefore, regulatory bodies should identify a minimum number of

KPIs to be issued by each firm in accordance with its sector. The definition and the

assumptions used to drive each of these KPIs should be unified and generalised for each

sector to enhance comparability between firms in the same sector.

This thesis has provided evidence that disclosure quality can be assessed based upon the

qualitative characteristics that are provided by the ASB (2006). This measure is reliable

and valid and can be used to evaluate disclosure quality for any type of narrative

disclosure. This measure of disclosure quality can be adopted by policy makers to

detect those areas of narrative disclosure which need more focus. Subsequently,

regulators could identify whether or not low disclosure quality is due to non-compliance

with the existing rules, or because of the absence of explicit guidance.

The empirical results have shown the weak impact of CG mechanisms upon firm value,

suggesting that investors might look at other firm attributes and board monitoring

mechanisms as substitutes. The results support the argument that firms with a


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concentration of power could achieve better performance. The positive impact of role

duality upon firm value suggests that a firm’s performance could be improved when it

is controlled by a CEO who has enough experience and knowledge about operating and

financial activities. The findings support the view that boards that are dominated by

NEDs might suffer from a lack of strategic decision making ability (Goodstein et al.,

1994), in addition to a lack of local experience and training in contrast to insider

directors (Dalton et al., 1999). These findings are in line with Laing and Weir (1999)

and Weir et al.’s (2002) studies that recommend to the regulators not to impose certain

CG structures on UK firms. Each firm would have a CG structure that is adapted to its

own characteristics and its surrounding environment. This also is in line with the

orientation of the CG code in the UK, which is dominated by comply or explain rule.

In line with this argument, this thesis provides strong evidence that particular CG

mechanisms affect the quantity and quality of KPI reporting. More specifically, it

informs regulatory bodies as well as information users, that firms with larger board size,

NED dominance, and higher NED compensation are more likely to report larger

numbers of KPIs with a higher level of quality. The results confirm that UK boards

perform a strong monitoring role, supporting the view that soft regulations in the UK do

not lead to a weakness in performing this role.

Moreover, firms which intend to issue bonds or loans tend to increase the quantity of

KPIs disclosed. Therefore, users should consider these attributes before taking any

decisions based on KPI analyses. This should be of interest to regulators as they could

encourage firms to improve these dominant mechanisms in particular, in order to

enhance KPI reporting.


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Rather, the findings might be important for shareholders and UK firms. The study

provides strong evidence that shareholders could improve KPI reporting and get better

firm value at the same time by offering higher compensation to non-executive directors

on the board.

KPI reporting quality has no statistical and significant relationship with firm value.

Notably, when considering the quantity of KPIs disclosed outside the KPI section in the

analyses, the negative association between KPI reporting quantity and firm value

becomes more significant

Moreover, the findings have potential managerial implications with regard to the

negative effect of the quantity of KPI reported upon firm value. In particular, the results

suggest that market participants should pay more attention to financial KPIs disclosed

in the annual report rather than non-financial ones. Hence, managers should be careful

about disclosing the basic KPIs. In other words, managers have to study carefully the

cost-benefit trade-off before increasing the number of KPIs disclosed.

5.5 Limitations of the study

The present study is one of the first to investigate the determinants as well as the value

and relevance of corporate reporting, distinguishing between disclosure quantity and its

quality. However, this thesis suffers from a number of limitations which represent good

avenues for future research.

One potential limitation is the relatively small sample size. This is a common limitation

of labour-intensive studies which employ manual content analysis to code the text. In

addition, CG data is collected by the researcher by reading firms’ annual reports. This

has reduced the ability to increase sample size due to time and effort considerations. In


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this regard, any firm without complete data time series was excluded following several

previous studies (e.g. Elshandidy et al., 2013). However, this may have caused survival-

ship bias because firms with missing data for more than one year were not allowed to

enter the sample. Data collection has led to another constraint with regard to the

variables included in the analyses. For instance, it was planned to examine the impact of

equity linked compensation on KPI reporting, but this variable has been excluded

during the analyses because of missing data problem.

In addition, small sample size has led to the selection of a very few firms with a low

number of observations in several sectors. Similarly, the sample period could not be

extended to longer than five years. As a result, the researcher could not draw

conclusions about industry effects based on empirical tests. In particular, this has

limited his ability to study in detail the impact of industry on KPI reporting quantity and

quality. This also has not enabled the researcher to conduct extensive analyses, testing

to what extent industry could affect firm value. Furthermore, the number of

observations has restricted the opportunity to obtain reliable results that could be

generalised with regard to the impact of financial crises periods on the results.

Furthermore, the descriptive statistics in chapter two showed that sample firms did not

consider most of the qualitative attributes recommended by the ASB (2006). Hence, the

resultant quality scores were heavily driven by two dimensions of the eight dimensions

suggested to measure reporting quality (providing the definition of each KPI and

quantifying the data). Therefore, caution should be exercised with regard to the

conclusions of KPI reporting quality. In particular, one might argue that UK firms do

not provide annual report users with useful information that could be incorporated in his

evaluation of a firm’s value. On the other hand, the descriptive statistics in chapter two


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showed that sample firms provided a limited number of non-financial KPIs with a

relatively low degree of reporting quality. Therefore, caution should be exercised with

regard to the conclusions with regard to non-financial KPI reporting determinants or

impacts.

Despite following different procedures to avoid multicollinearity among the dependent

variables, there is a high possibility that there is a circular causality between the

independent and dependent variables of the models. Therefore, the models employed

might suffer from the endogeneity problem.

The study focused on two types of KPI reporting (i.e. financial and non-financial KPI

information). One might argue that firms will not provide KPI information unless they

represent good news. In contrast, firms with disagreeable performance indicators will

not provide such information. The study has indirectly considered such news by

controlling for the impact of the main drivers for good and bad news, such as

profitability, liquidity and leverage. However, it might be helpful to study the tone of

KPI disclosures. It is expected that KPIs with good or bad news could have an influence

on investors’ valuation of the firm. More specifically, the analyses of the tone of KPI

disclosures could provide an extra explanation to the negative effect of KPI reporting

quantity on firm value.

Finally, there is another common limitation in studies that investigate the market

participants’ perceptions of information disclosed in the annual report. Market

participants could get similar information from other sources (e.g. online reporting,

analysts’ reports) before reading the annual report. That makes for a difficulty in

capturing the individual impact of that information as disclosed in the annual report.


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5.6 Suggestions for future research

The present study can provide good avenues for potential research. These opportunities

can be highlighted as follows:

Previous literature showed that the different indices used to measure disclosure quality

might lead to different results. Therefore, future studies could employ the instrument

used in this thesis to measure disclosure quality for other parts of narrative reporting

(e.g. risk disclosures). Then they could test whether the quantity and quality of such

disclosures are driven by the same factors. Future studies can also explore the separate

impact of each dimension on stock market participants.

In this regard, future research could improve disclosure measure used in this study. It is

suggested that future researchers consider the extent or richness of the KPI information

provided. Therefore, KPI quantity measures could be enhanced by considering the level

and range of relevant topics covered by KPI reporting. In addition, the study has

avoided any subjectivity that may be caused by weighting the type of KPI disclosed (i.e.

non-financial KPIs) or one attribute of those used to measure disclosure quality.

However, future studies could calculate weighted scores and compare the results with

the results produced using un-weighted scores. Arguably, quality measures might be

improved if weighting was given to particular dimensions such as quantifying KPI

targets and providing management commentary on it. As a result, KPI quality measures

could consider the depth of the KPI information disclosed, as it reflects the importance

of forward-looking (outlook) in this information.

Future research could explore firms’ practices with respect to other types of KPI

disclosures (e.g. KPI disclosures that include good news and ones that contain bad


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news, changes in KPI used from one year to another). This research opportunity will

show how firms could use KPI reporting content to send specific signals to investors.

On that basis, research could be extended to examine the determinants and impacts of

the tone of KPI disclosures.

Current research could be extended by many means. New variables could be introduced

to examine the extent to which these variables could affect KPI reporting. For example,

as audit committee meetings display their influence on KPI reporting quality, further

study could examine the effect of other audit committee characteristics such as the

financial expertise of the audit committee members. Moreover, some industries show a

remarkably low/high level of KPI reporting. Thus, a study could be conducted into such

sectors as Utilities, Basic Materials, Healthcare, Technology or Oil and Gas, to examine

the factors affecting the low/high level of quantity/quality in such sectors. In this

regard, sample size could be increased and the sample period can be extended, both of

which would add to the richness of the analysis.

Similarly, the impact of KPI reporting could be investigated from other perspectives.

More specifically, future research could study whether or not the quantity and quality of

KPI information have different effects on stock returns, cost of capital or on analyst

report accuracy. As mentioned above, the research could be extended by focusing on

some sectors in order to study the impact of KPI reporting by firms in these sectors

upon firms’ values.

Using qualitative research tools in measuring KPI disclosure quality represents another

direction for research. For example, undertaking interviews with investors might reveal

great insights into their perceptions with regard to the variation between firms in terms

of KPI quantity and quality.


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Finally, this research can be conducted in different contexts. There are new narrative

regulations that apply for periods ending on or after the end of September 2013. The

new regulations ask all UK firms with the exception of small ones, to replace the

business review with a strategic report. Therefore, future studies could address the same

research questions for periods beyond September 2013. Additionally, a comparative

study could be conducted among different European countries, to explore the variations

between EU countries in terms of KPI reporting quantity and quality after introducing

the business review regulation in 2003. It would be interesting to compare these

countries in terms of KPI reporting drivers and also their consequences. This would

confirm whether or not the findings of the present study are applicable to other

countries.

REFERENCES

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References

Abbott, L., Parker, S., and Peters, G. (2004), ‘Audit committee characteristics and

restatements’, Auditing: A Journal of Practice & Theory, Vol.23, No.1, pp. 69-87.

AbdelFattah, T., Dixon, R. and Woodhead, A. (2008), ‘Corporate governance and

voluntary disclosure in emerging capital markets: evidence from Egypt’, working

paper, Durham University, Durham.

Abdelsalam, O. and Street, D. (2007), ‘Corporate governance and the timeliness of

corporate internet reporting by U.K. Listed Companies’, Journal of International

Accounting, Auditing and Taxation, Vol. 16, No.2, pp. 111–130.

Aboody, D. and Kasznik, R. (2000), 'CEO stock option awards and the timing of

corporate voluntary disclosures', Journal of Accounting and Economics, Vol.29,

No.1, pp.73-100.

Abraham, S. and Cox, P. (2007), ‘Analysing the determinants of narrative risk

information in UK FTSE 100 annual reports’, The British Accounting Review,

Vol.39, No.3, PP. 227-248.

Accounting Standard Board (ASB) (2006), ‘Operating and Financial Review’, UK:

Accounting Standards Board.

Accounting Standards Board (ASB) (2009), 'A review of narrative reporting by UK

listed companies in 2008/2009', UK: Accounting Standards Board.

Acock, A. (2008), ' A gentle introduction to Stata', Second edition, Stata Press, US.

Advisory Committee on Improvements to Financial Reporting to SEC. (2008), ‘Final

Report of the Advisory Committee on Improvements to Financial Reporting to the

United States Securities and Exchange Commission’,pp.1-172.Available

at :http://www.sec.gov/about/offices/oca/acifr/acifr-finalreport.pdf. Last accessed

on 20-11-2012.

Aggarwal, R., Erel, I., Stulz, R. and Williamson, R. (2009),’Differences in Governance

Practices between U.S. and Foreign Firms’, Review of Financial Studies, Vol. 22, pp. 3131–316.

Agrawal, A. and Knoeber, C. (2001),'Do some outside directors play a political role?',

Journal of Law & Economics, Vol. 44, No.1,pp.179-198. Ahmed, K. and Courtis, K. (1999), ‘Associations between corporate characteristics and

disclosure levels in annual reports: a meta-analysis’, The British Accounting Review, Vol. 31 No. 1, pp. 35-61.

AICPA (1994), ‘Improving business reporting - a customer focus: Meeting the

information needs of investors and creditors, comprehensive report of the special committee on financial reporting (the Jenkins report)’, New York: American

Institute of Certified Public Accountants. Ajinkya, B., Bhojraj, S. and Sengupta, P. (2005), ‘The association between outside

directors, institutional investors and the properties of management earnings

forecasts’, Journal of Accounting Research, Vol. 43, No.3, pp. 343–376.

Al-Htaybat, K. (2005), ‘Financial disclosure practices: theoretical foundation, and an

empirical investigation on Jordanian printed and internet formats’, PhD Thesis,

University of Southampton, UK.

REFERENCES

Ap

pen

dic

es

227

Al-Najjar, B., Ding, R., and Hussainey, K. (2011), ‘CEO pay slice, corporate

governance disclosure and firm performance: UK evidence’, Working paper,

available at:

http://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0

CDEQFjAA&url=http%3A%2F%2Fbaa.group.shef.ac.uk%2Fdownload_papers%2

Findex.php%3Fsubmit%3Ddownload_file%26username%3Dbafa%26token%3D26

684b3867876df7bd3fcac34d8cbddb%26paper_id%3D102&ei=gqIPU776GurV0Q

WKxoHABA&usg=AFQjCNEANjIQX6AjmVe1AcNmqtuoDT8tlA&bvm=bv.619

65928,d.d2k

Aly, D., Simon, J. and Hussainey, K. (2010), ‘Determinants of corporate internet

reporting: evidence from Egypt’, Managerial Auditing Journal, Vol. 25, No.2, pp.

182-202.

Amihud, Y. and Mendelson, H. (1986), ‘Asset pricing and the bid-ask spread’. Journal

of Financial Economics, Vol.17, No.2, pp. 223–249.

Amir, E. and Lev, B. (1996), ‘Value-relevance of non-financial information: The

wireless communications industry’, Journal of Accounting and Economics, Vol.

22, pp. 3-30.

Ammann, M., Oesch, D., and Schmid, M. (2011), 'Corporate governance and firm

value: International evidence', Journal of Empirical Finance, Vol.18, No.1, pp.36-

55.

Anis, R., Fraser, I and Hussainey, K. (2010), 'Disclosure quality and corporate

governance mechanisms: a review and implications for future research ', Working

paper, University of Stirling, UK.

Archambault, J. and Archambault, M. (2003), ‘A multinational test of determinants of

corporate disclosure’, The International Journal of Accounting, Vol. 38, pp. 173-

194.

Baek, J., Kang, J. and Park, K. (2004), ‘Corporate governance and firm value: evidence

from the Korean financial crisis’, Journal of Financial Economics , Vol. 71, No. 2,

pp. 265-313.

Ball, R. (1978), ‘Anomalies in relationships between securities’ yields and yields

surrogates’, Journal of Financial Economics, Vol.6, No.2, pp.103-126.

Ball, R., Foster, G., (1982), ‘Corporate financial reporting: a methodological review of

empirical research’, Journal of Accounting Research, Vol. 20 (Supplement),

pp.161-234.

Barton, J., Bowe, T. and Pownall, G. (2010),' Which performance measures do

investors around the world value the most and why?', The Accounting Review, Vol.

85, No. 3, pp. 753-789.

Beattie, V. (1999), ‘Business reporting: the inevitable change’, Research Report,

Edinburgh: Institute of Chartered Accountants of Scotland (ICAS).

Beattie, V. and Jones, M. (1992), 'The use and abuse of graphs in annual reports:

theoretical framework and empirical study', Accounting and Business Research,

Vol. 22, No.88, pp. 291-303.

http://www.sciencedirect.com/science?_ob=JournalURL&_cdi=5938&_auth=y&_acct=C000046145&_version=1&_urlVersion=0&_userid=855330&md5=c802e6e578d14b6de11d2c5f6cd3409a

http://www.accfin.gla.ac.uk/AccFin/StaffProfile.aspx?TabID=1&MenuID=5&Username=vbeattie

REFERENCES

Ap

pen

dic

es

228

Beattie, V. and Pratt, K. (2002), ‘Voluntary annual report disclosures: what users

want’, Research Report, Edinburgh: Institute of Chartered Accountants of Scotland

(ICAS).

Beattie, V., McInnes, B. and Fearnley, S. (2004), ‘A methodology for analysing and

evaluating narratives in annual reports: a comprehensive descriptive profile and

metrics for disclosure quality attributes’, Accounting Forum, Vol.28, pp. 205-236.

Beattie, V. and Thomson, S. (2007), ‘Lifting the lid on the use of content analysis to

investigate intellectual capital disclosures’, Accounting Forum, Vol. 31, No.2, pp.

129-163.

Beest, F., and Braam, G. (2011), 'An empirical analysis of quality differences between

UK annual reports and US 10-K reports', Working Paper, https://da6d86ff-a-

62cb3a1a-s-sites.googlegroups.com/site/accountingandcontrolchange/racc-

conferences/

Beretta, S., and Bozzolan, S. (2004), ‘A framework for the analysis of firm risk

communication’, The International Journal of Accounting, Vol.39, No.3, pp. 265-

288.

Beretta, S. and Bozzolan, S. (2008), ‘Quality versus quantity: the case of forward-

looking disclosure’, Journal of Accounting, Auditing and Finance, Vol.23, No.3,

pp. 333-375.

Berger, P. (2011), 'Challenges and opportunities in disclosure research: A discussion of

the financial reporting environment: Review of the recent literature', Journal of

Accounting and Economics, Volume 51, No. 1–2, pp.204–218

Beyer, A., Cohen, D., Lys, T., and Walther, B. (2010) ' The financial reporting

environment: Review of the recent literature', Journal of Accounting and

Economics, Vol.50, No. (2/3), pp. 296-343.

Bloomfield, R. and Wilks, T. (2000), ‘Disclosure effects in the laboratory: liquidity,

depth, and the cost of capital’, The Accounting Review, Vol. 75, No. 1, pp. 13-41.

Boesso, G. and Kumar, K.(2007), ‘Drivers of corporate voluntary disclosure: a

framework and empirical evidence from Italy and the United States’, Accounting,

Auditing and Accountability Journal, Vol. 20 No.2, pp. 269- 296.

Boesso, G. and Kumar, K. (2009), ‘Stakeholder previousitization and reporting:

evidence from Italy and the US’, Accounting Forum, Vol.33, pp.162–175.

Booker, D., Heitger, D. and Schultz, T. (2010), ‘The effect of causal knowledge on

individuals' perceptions of nonfinancial performance measures in profit prediction’,

Advances in Accounting, Vol.27, No. 1 , pp. 90-98.

Boubaker, S., Lakhal, F. and Nekhili, M. (2011), 'The determinants of web-based corporate reporting in France', Managerial Auditing Journal, Vol. 27, No.2, pp. 126-155.

Botosan, C. (1997), ‘Disclosure level and the cost of equity capital’, The Accounting

Review, Vol. 72, No.3, pp. 323-349.

Botosan, C. (2004), ‘Discussion of a framework for the analysis of firm risk

communication’, The International Journal of Accounting, Vol.39, No.3, pp. 289-

295.

http://www.accfin.gla.ac.uk/AccFin/StaffProfile.aspx?TabID=1&MenuID=5&Username=vbeattie

REFERENCES

Ap

pen

dic

es

229

Botosan, C. and Plumlee, M. (2002), ‘A re-examination of disclosure level and the

expected cost of equity capital’, Journal of Accounting Research, Vol. 40, No.1,

pp. 21-40.

Bravo, F, Abad, M. and Trombetta, M. (2009), 'Disclosure indices design: Does it make

a difference?, Spanish Accounting Review, Vol.12, No.2, pp.253-277.

Bray, M. (2010), ‘The journey to better business reporting: moving beyond financial

reporting to improve investment decision making’, KPMG, Australia.

Breton, G. and Taffler, J. (2001), ‘Accounting information and analyst stock

recommendation decisions: a content analysis approach’, Accounting and Business

Research, Vol. 31, No.2, pp. 91-101.

Brown, S. and Warner, J. (1980), ‘Measuring security price performance’, Journal of

Financial Economics, Vol. 8, No. 3, pp.205-258.

Bruce, A., Skovoroda, R., Fattorusso, J., and Buck, T. (2007), ‘Executive bonus and

firm performance in the UK’, Long Range Planning, Vol.40, pp. 280-294.

Bushee, B., Leuz, c. (2005), 'Economic consequences of SEC disclosure regulation:

evidence from the OTC bulletin board', Journal of Accounting and Economics,

Vol.39, pp.233-264

Cadbury Report (1992), ‘Report of the committee on the financial aspects of corporate

governance’, London. Available at:

http://www.ecgi.org/codes/documents/cadbury.pdf

Carcello, J., and Neal, T. (2003), Audit committee independence and disclosure: Choice

for financially distressed firms, Corporate Governance: An International Review,

Vol. 11, pp. 289–299.

Chau, G. and Gray, S. (2002), ‘Ownership structure and corporate voluntary disclosure

in Hong Kong and Singapore’, The International Journal of Accounting, Vol.37,

pp.247-265.

Chen, W., Lin, B. and Yi, B. (2008), 'CEO duality and firm performance – an

endogenous issue’, Corporate Ownership and Control, Vol. 6 No. 1, pp. 58-65.

Cheng, E. and Courtenay, S. (2006), ‘Board composition, regulatory regime and

voluntary disclosure’, The International Journal of Accounting, Vol. 41, No. 3, pp.

262-289.

Cheng, P., Su, L. and Zhu, X. (2012), ' Managerial ownership, board monitoring and firm performance in a family-concentrated corporate environment, Accounting and

Finance, Vol. 52, pp. 1061–1081. Choi, F. (1973), ‘Financial disclosure and entry to the European capital market’,

Journal of Accounting Research, Vol.11, No.2, pp.159-175.

Chung, J., Kim, H., Kim, W., Yoo, Y. (2012), 'Effects of disclosure quality on market

mispricing: evidence from derivative-related loss announcements', Journal of

Business Finance and Accounting, Vol.39, No.7-8, pp936-959.

Clarkson, M. (1995), ‘A stakeholder framework for analyzing and evaluating corporate

social performance’, Academy of Management Review, Vol. 20 No. 1, pp.92-117.

Clarkson, P., Fang, X., and Li, Y. (2013), ‘The relevance of environmental disclosures:

Are such disclosures incrementally informative?’, Journal of Accounting and Public Policy, Vol. 32, No. 5, pp.410-431.

http://web.ebscohost.com.ezproxy.stir.ac.uk/ehost/viewarticle?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46a9Mr6uwTa6k63nn5Kx95uXxjL6trUuvpbBIr6eeSriotlKzq55oy5zyit%2fk8Xnh6ueH7N%2fiVa%2busUyzr7VNr6qkhN%2fk5VXj5KR84LPgjeac8nnls79mpNfsVberrkuwra9Rt6qkfu3o63nys%2bSN6uLyffbq&hid=126

http://web.ebscohost.com.ezproxy.stir.ac.uk/ehost/viewarticle?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46a9Mr6uwTa6k63nn5Kx95uXxjL6trUuvpbBIr6eeSriotlKzq55oy5zyit%2fk8Xnh6ueH7N%2fiVa%2busUyzr7VNr6qkhN%2fk5VXj5KR84LPgjeac8nnls79mpNfsVberrkuwra9Rt6qkfu3o63nys%2bSN6uLyffbq&hid=126

REFERENCES

Ap

pen

dic

es

230

Clarkson, P., Guedes, J. and Thompson, R. (1996), ‘On the diversification,

observability and measurement of estimation risk’, Journal of Financial and

Quantitative Analysis, Vol.31, No.1, pp. 69–84.

Clarkson, P., Li, Y., and Richardson, G., (2004),'The market valuation of environmental

capital expenditures by pulp and paper companies, The Accounting Review, Vol.79,

No.2, pp.329-353.

Clarkson, P. and Satterly, A. (July, 1997), ‘Australian evidence on the pricing of

estimation risk’, Pacific-Basin Finance Journal, Vol. 5, No. 3, pp. 281-299.

Coles, J., Loewenstein, U. and Suay, J. (1995). ‘On equilibrium pricing under parameter

uncertainty’, Journal of Financial and Quantitative Analysis, Vol.30, No.3, pp.

347–364.

Collier, P. and Gregory, A. (1996), ‘Audit committee effectiveness and the audit fee’,

European Accounting Review, Vol.5, No.2, pp. 177-198.

Companies Act 2006, available at: www.legislation.gov.uk

Conger, J., Finegold, D., and Lawler III, E.(1998), 'Appraising boardroom

performance', Harvard Business Review, Vol. 76, No.1, pp.136-148.

Conover, T. and Wallace, W. (1995), ‘Equity market benefits to disclosure of

geographic segment information: an argument for decreased uncertainty’, Journal

of International Accounting, Auditing and Taxation, Vol.4, No.2, pp.101-112.

Cooke, T. (1989), ‘Disclosure in the corporate annual report of Swedish companies’,

Accounting & Business Research, Vol. 19, No.34, pp. 113-22.

Cooke, T., (1991), ‘An assessment of voluntary disclosure in the annual reports of

Japanese corporations’, International Journal of Accounting, Vol. 26, No. 3, pp.

174-89.

Cooke, T. (1992), ‘The impact of size, stock market listing and industry type on

disclosure in the annual reports of Japanese listed corporations’, Accounting and

Business Research, Vol. 22, pp. 229-237.

Cooke, T. (1993), ‘Disclosure in Japanese corporate annual reports’, Journal of

Business Finance and Accounting, Vol.20, No.4, pp. 521-536.

Cooke, T. (1998), 'Regression analysis in accounting disclosure studies', Accounting

and Business Research, Vol.28, pp.209-224.

Core, J. (2001), ‘A review of the empirical disclosure literature: discussion’, Journal of

Accounting and Economics, Vol. 31, No.1-3, pp.441–456.

Craven, B. and Marston, C. (1999), ‘Financial reporting on the internet by leading UK

Companies’, The European Accounting Review, Vol. 8, No. 2, pp. 321-33.

Cuzick, J. (1985), ‘A Wilcoxon-type test for trend’, Statistics in Medicine, Vol.4, No.4,

pp.543-547.

Dalton, D., Daily, C., Ellstrand, A. and Johnson, J. (1999), ‘Number of directors and

financial performance: A meta-analysis’, Academy of Management Journal,

Vol.42, No.6, pp.674–686.

Dechow, P., and Dichev, I. (2002), ' The quality of accruals and earnings: The role of

accrual estimation errors', The Accounting Review, Vol. 77 (Supplement), pp. 35–

59.

http://www.legislation.gov.uk/

REFERENCES

Ap

pen

dic

es

231

Deegan, C., Rankin, M. and Tobin, J. (2002), ‘An examination of the corporate social

and environmental disclosures of BHP from 1983-1997’, Accounting, Auditing and

Accountability Journal, Vol.15, No. 3, pp. 312-343.

Deegan, C. and Unerman, J. (2006), ‘Financial accounting theory: European edition’,

UK: McGraw-Hill Education.

Deloitte (2006), Write to reason: Surveying OFRSs and narrative reporting in annual

reports, Deloitte, London.

Deloitte (2009), ‘A telling performance surveying narrative reporting in annual reports’,

Deloitte, London, Available at: http://www.deloitte.com/assets/Dcom-

UnitedKingdom/

Deloitte (2010), ' IFRS in Focus: IASB completes first phase of joint project on

Conceptual Framework for Financial Reporting, Available at:

http://www.iasplus.com/en/publications/global/ifrs-in-focus/2010/ifrs-in-focus-

2014-iasb-completes-first-phase-of-joint-project-on-conceptual-framework-for-

financial-reporting-october-2010.

Dempsey, S., Gatti, J., Grinell, D. and Cats-Baril, W. (1997), ‘ The use of strategic

performance variables as leading indictors in financial analysts’ forecasts’, The

Journal of Financial Statement Analysis, Vol. 2, No.4.pp. 61-79.

Department for Business, Innovation and Skills (2010), ‘The future of narrative

reporting: A consultation’, BIS, Crown Copyright, available at: www.bis.gov.uk

Dhaliwal, S., Li, Z., Tsang, A. and Yang, G. (2011), 'Voluntary nonfinancial disclosure

and the cost of equity capital: The initiation of corporate social responsibility

reporting', The Accounting Review, Vol.86, No.1, pp. 59-100.

Dhaliwal, D., Spicer, B. and Vickrey, D. (1979), ‘The quality of disclosure and the cost

of capital’, Journal of Business Finance and Accounting, Vol.6, No. 2, pp. 245-

265.

Diamond, D., and Verrecchia, R. (1991),' Disclosure, liquidity and the cost of capital',

The Journal of Finance, Vol.66, pp.1325-1360.

Dorestani, A. and Rezaee, Z. (2011a),'Key performance indicators and analysts'

earnings forcast accuracy: An application of content analysis', Asian Academy of

Management Journal of Accounting and Finance, Vol.7, No2, pp. 79-102.

Dorestani, A. and Rezaee, Z. (2011b), 'Non-financial key performance indicators and

quality of Earnings', Journal of Accounting and Financ, Vol. 11, No. 3, pp. 75 - 96.

Drake, M., Myers, J. and Myers, L. (2009), 'Disclosure quality and the mispricing of

accruals and cash flow', Journal of Accounting, Auditing, and Finance, pp.357–

384.

El-Gazzar, S. (1998), ‘Pre-disclosure information and institutional ownership: a cross-

sectional examination of market revaluations during earnings announcement

periods’, The Accounting Review, Vol. 73, No.1, pp. 119-129.

Elshandidy, T., Fraser, I., Hussainey, K. (2013), ‘Aggregated, voluntary, and

mandatory risk disclosure incentives: Evidence from UK FTSE all-Share

companies’, International Review of Financial Analysis, Forthcoming.

http://www.deloitte.com/assets/Dcom-UnitedKingdom/Local%20Assets/Documents/Services/Audit/Corporate%20Governance/UK_Audit_A_telling_performance.pdf

http://www.deloitte.com/assets/Dcom-UnitedKingdom/Local%20Assets/Documents/Services/Audit/Corporate%20Governance/UK_Audit_A_telling_performance.pdf

REFERENCES

Ap

pen

dic

es

232

Elzahar, H. and Hussainey, K. (2012), ' Determinants of narrative risk disclosures in

UK interim reports. Journal of Risk Finance, Vol. 13, No. 2, pp. 133-147.

Eng, L. and Mak, Y. (2003), ‘Corporate governance and voluntary disclosure’, Journal

of Accounting and Public Policy, Vol. 22, No.4, pp. 293-376.

Eng, L. and Teo, H. (2000), ‘The relation between annual report disclosures, analysts’

earnings forecasts and analyst following: Evidence from Singapore’, Pacific

Accounting Review, Vol.11, No.1, pp. 219-239.

Epstein, M. and Palepu, K. (1999), ‘What financial analysts want’, Strategic Finance,

(April), pp.42-52.

Ezat, A. (2010),'The key determinants of the voluntary adoption of corporate internet

reporting and its consequences on firm value: Evidence from Egypt', PhD Thesis,

University of Plymouth, UK.

Fama, E. (1970), ‘Efficient capital markets: A review of the theory and empirical work’, The

Journal of Finance, Vol.25,No.2,pp.383-415.

Fama, E. and Jensen, M. (1983), ‘Separation of ownership and control’, Journal of Law

& Economics, Vol.26, No.4, pp.9-15.

Felo, A., Krishnamurthy, S. and Solieri, S. (April 2003), ‘Audit committee

characteristics and the perceived quality of financial reporting: an empirical

analysis’, Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=401240

Financial Reporting Council (2009), ‘A Review of Narrative Reporting by UK Listed

Companies in 2008/2009’, pp.1-45.Available at:

http://www.frc.org.uk/images/uploaded/documents/Full%20results%20of%20a%20

Review%20of%20narrative%20reporting1.pdf, Accessed on 28 -10- 2010.

Financial Reporting Council (2012), ‘Guidance on audit committees', London: FRC.

Available at: http://www.frc.org.uk/getattachment/6ec23196-28ee-406e-8f56-

89ab9d1dc06d/Guidance-on-Audit-Committees-September-2012.aspx

Firth, M. (1980), ‘Raising finance and firms' corporate reporting policies’, Abacus,

Vol.16, No.2, pp.100-115.

Firth, M. (1984), ‘The extent of voluntary disclosure in corporate annual reports and its

association with security risk measures’, Applied Economics, Vol.16, No. 2,

pp.269-277.

Forker, J. (1992), ‘Corporate governance and disclosure quality’, Accounting and

Business Research, Vol. 22, No.86, pp.111-124.

Foster, G. (1986), ‘Financial Statement Analysis’, New Jersey: Prentice-Hall.

Francis, J., Hanna, J. and Philbrick, D. (1997), ‘Management communications with

securities analysts’, Journal of Accounting and Economics, Vo. 24, No.3, pp. 363–

394.

Gelb, D. and Zarowin, P. (2002), ‘Corporate Disclosure Policy and the Informativeness

of Stock Prices’, Review of Accounting Studies, Vol. 7, pp. 33–52.

Ghazali, N. and Weetman, P. (2006), ‘Perpetuating traditional influences: voluntary

disclosure in Malaysia following the economic crisis’, Journal of International

Accounting, Auditing and Taxation, Vol.15, No. 2, pp.226-248.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=401240

REFERENCES

Ap

pen

dic

es

233

Giunta, F., Dainelli, F., Guidantoni, S. and Bini, L. (2008), ‘The international

standardization for communicating financial key performance indicators: evidence

from Italy’, Working paper, University of Florence, Italy.

Goodstein, J., Gautum, K., and Boeker, W. (1994), ‘The Effect of board size and

diversity on strategic change’, Strategic Management Journal, Vol. 15, pp. 241–

250.

Gow, I. (2010), ‘ Correcting for cross-sectional and time series dependence in

accounting research, The Accounting Review, Vol.85,No.2,pp.483-521.

Graham, J., Harvey, C., Rajgopal, S. (2005), ‘The economic implications of corporate

financial reporting’, Journal of Accounting and Economics, Vol. 40, No.1-3, pp. 3–

73.

Gray, R., Kouhy, R. and Lavers, S. (1995), ‘Corporate social and environmental

reporting: a review of the literature and a longitudinal study of UK disclosure’,

Accounting, Auditing and Accountability Journal, Vol. 8, No. 2, pp. 47- 77.

Gray, S., Meek, G. and Roberts, C.( 1995), ‘International capital market pressures and

voluntary annual report disclosures by U.S. and U.K. multinationals’, Journal of

International Financial Management and Accounting, Vol.6, No.1, pp.1-27.

Grey, C., Stathopoulos, K. and Walker, M. (2012), ' The impact of executive pay on the

disclosure of alternative earnings per share figures', International Review of

Financial Analysis, Forthcoming.,

Guest, P. (2008), ‘The determinants of board size and composition: evidence from the

UK”, Journal of Corporate Finance, Vol.14, pp.51–72.

Gujarati, D., (2003),' Basic econometrics', fourth edition, McGrawHill, USA.

Haggard, S., Martin, X. and Pereira, R. (2008), ‘Does voluntary disclosure improve

stock price informativeness?', Financial Management, Winter, pp.747 – 768.

Hail, L. (2002), ‘The impact of voluntary corporate disclosures on the ex ante cost of

capital for Swiss firms’, European Accounting Review, Vol.11,No.4, pp. 741- 773.

Haniffa, R. and Cooke, T. (2002), “'Culture, Corporate Governance and Disclosure in

Malaysian Corporations ', ABACUS, Vol.38, No.3, pp. 317-349.

Hamilton, L. (2006), 'Statistics with Stata: updated for version 9', Curt Hinrichs,

Canada.

Harvey, I. (2004), ‘Analytical quality glossary’, available at:

http://www.qualityresearchinternational.com/glossary/#p last accessed on 5th of

July 2011.

Hassan, O. (2006), ‘The economic consequences of increased disclosure in the Egyptian

emerging capital market’, PhD Thesis, University of Abertay, Dundee, UK.

Hassan, O., Giorgioni, G. and Romilly, P. (2006), ‘The extent of financial disclosure

and its determinants in an emerging capital market: the case of Egypt’,

International Journal of Accounting, Auditing and Performance Evaluation, Vol. 3

No. 1, pp. 41-67.

Hassan, O., Romilly, P., Giorgioni, G. and Power, D. (2009), ‘The value relevance of

disclosure: Evidence from the emerging capital market of Egypt, The International Journal of Accounting, Vol. 44, PP. 79–102

http://www.qualityresearchinternational.com/glossary/#p

REFERENCES

Ap

pen

dic

es

234

Hassan, O. and Marston, C. (2010), ‘Disclosure measurement in the empirical accounting literature: a review article’, working paper, Economics and Finance’,

Working Paper Series, Brunel University, available at: http://www.brunel.ac.uk/about/acad/sss/depts/economics

Healy, P., Hutton, A., and Palepu, K. (1999) ‘Stock performance and intermediation

changes surrounding sustained increases in disclosure’, Contemporary Accounting

Research, Vol.16, No.3, pp. 485-520.

Healy, P. and Palepu, K. (1993), ‘The effect of firms’ financial disclosure strategies on

stock prices’, Accounting Horizons, Vol. 7, No. 1, pp.1-11.

Healy, P. and Palepu, K. (2001), ‘Information asymmetry, corporate disclosure, and the

capital markets: A review of the empirical disclosure literature’, Journal of

Accounting and Economics, Vol. 31, No.1-3, pp.405–440.

Heitzman, S.,Wasley, C. and Zimmerman, J. (2010), 'The joint effects of materiality

thresholds and voluntary disclosure incentives on firm’s disclosure decisions',

Journal of accounting and economics, Vol. 49, 31, No. 1–2, pp.109–132.

Ho, S. and Wong. (2001), ‘A Study of the relationship between corporate governance

structures and the Extent of voluntary disclosure’, Journal of International

Accounting, Auditing and Taxation, Vol.10, No.1, pp. 139-156.

Hodgson, A. and Stevenson-Clarke, P. (2000), ‘Accounting variables and stock returns:

The impact of leverage’, Pacific Accounting Review, Vol.12, No.2, pp. 37-64.

Holland, J. (1998), ‘Private voluntary disclosure, financial intermediation and market

efficiency’, Journal of Business Finance and Accounting, Vol. 25, pp. 29–68.

Hope, O. (2003), ‘Disclosure practices, enforcement of accounting standards, and

analysts' forecast accuracy: an international study’, Journal of Accounting

Research, Vol. 41, No.2, pp.235-272.

Hopkins, P. (1996), ‘The effect of financial statement classification of hybrid financial

instruments on financial analysts’ stock price judgements’, Journal of Accounting

Research, 34 (Supplement),pp.33-50.

Hossain, M., Perera, B. and Rahman, R. (1995), ‘Voluntary disclosure in the annual

reports of New Zealand companies’, Journal of International Financial

Management and Accounting, Vol. 6, pp. 69-87.

Hribar, P., Kravet, T., and Wilson, R. (2010), 'A New Measure of Accounting Quality',

Working Paper, Available at SSRN: http://ssrn.com/abstract=1283946

Hughes, P. (1986), ‘Signalling by direct disclosure under asymmetric information’,

Journal of Accounting and Economics, Vol.8, No.2, pp.119-142.

Hussainey, K. (2004), ‘A study of the ability of (partially) automated disclosure scores

to explain the information content of annual report narratives for future earnings’,

PhD Thesis, Manchester University, UK.

Hussainey, K., and Al-Najjar, B. (2011), ‘Future-oriented narrative reporting:

determinants and use’, Journal of Applied Accounting Research, forthcoming.

Hussainey, K. and Mouselli, S.(2010), 'Disclosure quality and stock returns in the UK',

Journal of Applied Accounting Research, Vol.11, No.2, pp.154-174.

http://www.brunel.ac.uk/about/acad/sss/depts/economics

REFERENCES

Ap

pen

dic

es

235

Hussainey, K., Schleicher, T. and Walker, M. (2003), ‘Undertaking large-scale

disclosure studies when AIMR-FAF ratings are not available: the case of prices

leading earnings’, Accounting and Business Research, Vol.33, No. 4, pp. 275-294.

Hussainey, K., and Walker, M. (2006), ‘The use of forward looking key performance

indicators by sell-side financial analysts’, working paper.

Hussainey, K., and Walker, M. (2009), ‘The effects of voluntary disclosure and

dividend propensity on prices leading earnings’, Accounting and Business

Research, Vol. 39, No.1, pp.37-55.

IASB (1989), ‘The framework for the preparation and presentation of financial

statements’, International Accounting Standards Committee Foundation.

IASB (2010), ‘Management Commentary: A framework for presentation’, International

Accounting Standard Board, London.

IASB (2010), 'Conceptual framework for financial reporting', International Accounting

Standards Board, London.

Ittner, C., and Larcker, D. (1998), ‘Are nonfinancial measures leading indicators of

financial performance? : an analysis of customer satisfaction’, Journal of

Accounting Research, Vol. 36, pp. 1-35.

Jensen M. and Meckling, W. (1976), “Theory of the firm: managerial behaviour,

agency costs and ownership structure, Journal of Financial Economics, Vol. 3,

pp.305-360.

Jiang, H. and Habib, A. (2009),' The impact of ownership concentration on voluntary

disclosure practices in New Zealand', Accounting Research Journal, Vol.

22,No.3, pp. 275-304.

Jonas, A. (2007), 'An empirical study of executive management team compensation and

company performance', PhD Thesis, Virginia Commonwealth University

Richmond, Virginia.

Kelton, A, and Yang, Y. (2008), ‘The impact of corporate governance on Internet

financial reporting', Journal of Accounting and Public Policy, Vol.27, No.1, pp.

62–87.

Klein, A. (1998), ‘Firm performance and board committee structure’, Journal of Law

and Economics, Vol. 41, No. 1, pp. 275–299.

Klein, A. (2002), 'Audit committee, board of director characteristics, and earnings

management', Journal of Accounting and Economics, Vol. 33, pp.375–400.

Kiel, G and Nicholson, G. (2003),'Board composition and corporate performance: How

the Australian experience informs contrasting theories of corporate governance',

Corporate Governance: An International Review, Vol. 11, No.3, pp. 189-206.

Kothari, P., Li, X. and Short, E.(2009), 'The effect of disclosures by management,

analysts, and business press on cost of capital, return volatility, and analyst

forecasts: a study using content analysis', Accounting Review, Vol.84, No.5, pp.

1639–1670.

Krippendorff, K. (1980). “Content analysis: An introduction to its methodology’,

Beverly Hills, CA: Sage Publications.

REFERENCES

Ap

pen

dic

es

236

Lakhal, F. (2005), ‘Voluntary earnings disclosures and corporate governance: evidence

from France’, Review of Accounting and Finance, Vol.4, No. 3, PP. 64-85.

Laksmana, I. (2008), ‘Corporate board governance and voluntary disclosure of

executive compensation practices’, Contemporary Accounting Research, Vol.25,

No.4, pp. 47-82.

Lang, M. and Lundholm, R. (1993), ‘Cross-sectional determinants of analyst ratings of

corporate disclosures’, Journal of Accounting Research, Vol.31, No, 2, pp. 246-

271.

Lang, M. and Lundholm, R. (1996), ‘Corporate disclosure policy and analyst

behaviour’, The Accounting Review, Vol. 71, No.4, pp. 467-492.

Lang, M. and Lundholm, R. (2000), ‘Voluntary disclosure and equity offerings: reducing information asymmetry or hyping the stock’, Contemporary Accounting Research, Vol.17, No.4, pp.623-669.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R.( 2002),’ Investor protection and corporate valuation, Journal of Finance ,Vol. 57, pp. 1147–1170.

Leftwich, R., Watts, R. and Zimmerman, J. (1981), ‘Voluntary corporate disclosure: the case of interim reporting’, Journal of Accounting Research, Supplement to Vol.19, pp.50–77.

Lev, B., and Zarowin, P. (1999), ‘The boundaries of financial reporting and how to

extend them’, Journal of Accounting Research, Vol. 37, No. 2, pp. 353-385.

Li, J., Mangena, M. and Pike, R. (2012), ' The effect of audit committee characteristics

on intellectual capital disclosure', The British Accounting Review, Vol. 44,pp. 98–

110

Li, J., Pike, R .and Haniffa, R.(2008), ‘Intellectual capital disclosure and corporate

governance structure in UK firms’, Accounting and Business Research, Vol. 38,

No. 2. pp. 137-159.

Lins, K. (2003), ‘Equity ownership and firm value in emerging markets’, Journal of

Financial and Quantitative Analysis, Vol. 38, No. 1, pp.159-184.

Linsley, P. and Shrives, P. (2006), ‘Risk reporting: a study of risk disclosures in the

annual reports of UK companies’, The British Accounting Review, Vol.38, No.4,

pp. 387-404.

Malkiel, B. (2003), ‘The efficient market hypothesis and its critics’, Journal of

Economic Perspectives, Vol. 17, No. 1, pp. 59 – 82.

Malone, D., Clarence, F. and Jones, T. (1993), ‘An empirical investigation of the extent

of corporate financial disclosure in the Oil and Gas Industry’, Journal of

Accounting, Auditing and Finance, Vol. 8, No. 3, p249-275.

Mangena, M. and Pike, R. (2005), ‘The effect of audit committee shareholding,

financial expertise and size on interim financial disclosures’, Accounting and

Business Research, Vol.35, No.4, pp. 327–349.

Marston C. and Shrives P. (1991). ‘The use of disclosure indices in accounting

research: a review article’, British Accounting Review, Vol.23, pp. 195–210.

REFERENCES

Ap

pen

dic

es

237

Mavrinac, S., and Seisfeld, T. (1998), ‘Measures that matter: An exploratory

investigation of Investors' information needs and value previousities’, Working

paper, Ernst & Young.

Mendes-Da-Silva, W. and De Lira Alves, L. (2004), ‘The voluntary disclosure of

financial information on the internet and the firm value effect in companies across

Latin America’, Working Paper, Universidad Navarra Barcelona, 13th International

Symposium on Ethics, Business and Society, available at:

http://ssrn.com/abstract=493805, last accessed on 21st of May 2011.

Merck, R., Shleifer, A. and Vishny, R. (1988), “Management ownership and market

valuation: an empirical analysis,” Journal of Financial Economics, Vol. 20, pp.

293-315.

Milne, M. and Adler, R. (1999), ‘Exploring the reliability of social and environmental

disclosure content analysis’, Accounting, Auditing, and Accountability Journal,

Vol. 12, No. 2, pp.237–256.

Morris, R. (1987), ‘Signaling, agency theory and accounting policy choice’, Accounting

and Business Research, Vol.18, No.69, pp.47-56.

Mouselli, S. and Hussainey, K. (2010). 'Disclosure quality and stock returns in the UK',

Journal of Applied Accounting Research, Vol. 11, No.2, pp. 154-174.

Mouselli, S., Jaafar, A. and Hussainey, K. (2012), ‘Accrual quality vis-à-vis disclosure

quality: Substitutes or complements?’, The British Accounting Review,

Vol.44,No.1, pp. 36-46.

Murray, A., Sinclair, D., Power, D. and Gray, R. (2006), ‘Do financial markets care about

social and environmental disclosure? : Further evidence and exploration from the UK’,

Accounting, Auditing and Accountability Journal, Vol. 19 No. 2, pp. 228-55.

Nagar, V., Nanda, D. and Wysocki, P. (2003), 'Discretionary disclosure and stock-based

incentives', Journal of Accounting and Economics,Vol. 34, No. 1–3, pp. 283–309.

Naser, K., Al-Hussaini, A., Al-Kwari, D. and Nuseibeh, R. (2006), ‘Determinants of

corporate social disclosure in developing countries: the case of Qatar’, Advances in

International Accounting, Vol. 19, pp. 1-23.

Neely, A., Gregory, M., and Platts, K. (1995), 'Performance measurement system

design: a literature review and research agenda', International Journal of

Operations and Production Management, Vol. 15, No. 4, pp. 80-116.

Nelson, J., Gallery, G., and Percy, M. (2010), ‘Role of corporate governance in

mitigating the selective disclosure of executive stock option information’,

Accounting & Finance, Vol.50, No.3, pp.685-717.

Neu, D., Warsame, H. and Pedwell, K. (1998), ‘Managing Public impressions:

environmental disclosures in annual reports’, Accounting, Organizations and

Society, Vol. 23, No. 3, pp. 265-282.

Nielsen, C. (2010), ‘Making management commentary matter to the investment

community’, Working paper, Aalborg University, Denmark.

Organization for Economic Co-operation and Development (OECD) (2004), ‘OECD

Principles of Corporate Governance’, OECD, Paris. Available at:

http://www.oecd.org/dataoecd/32/18/31557724.pdf

http://ssrn.com/abstract=493805

http://www.oecd.org/dataoecd/32/18/31557724.pdf

REFERENCES

Ap

pen

dic

es

238

Owusu-Ansah, S. (2000), ‘Timeliness of corporate financial reporting in emerging

capital markets: Emprical evidence from the Zimbabwe stock exchange’,

Accounting and Business Research, Vol. 30, No.3, pp.241-254.

Oyelere, P., Laswad, F. and Fisher, R. (2003), ‘ Determinants of internet financial

reporting by New Zealand Companies’, Journal of International Financial

Management and Accounting, Vol. 14, No. 1, pp. 26-63.

Pankoff, L. and Virgil, R. (1970), ‘Some preliminary findings from a laboratory experiment on the usefulness of financial accounting information to security

analysts’, Journal of Accounting Research, Vol.8 (Supplement), pp. 1- 48.

Pallant, J., (2005), ' SPSS Survival manual: a step by step guide to data analysis using

SPSS version 12', Second edition, Open University Press, USA. Parmenter, D.(2010), ‘Key performance indicators: developing, implementing, and

using winning KPI’, John Wiley&Sons, Inc., Hoboken, New Jersey.

Pratt, K. and Beattie, V. (2002),'Voluntary annual report disclosures: What users want ',

Edinburgh: The Institute of Chartered Accountants of Scotland.

Patten, D. (2005),'The accuracy of financial report projections of future environmental

capital expenditure: A research note', Accounting Organisation and Society, Vol.

30, No. 5, pp.457-468.

PriceWaterHouseCoopers (2006), ‘Guide to key performance indicators:

Communicating the measures that matter’, available at:http://www.corpora-

tereporting.com/global_KPI_guide.pdf].

Rawlings, J., Pentula, S. and Dickey, D. (1998), 'Applied regression analysis: a

research tool', Second edition, Springer-Verlag New York, Inc., USA.

Roberts, R.W. (1992). Determinants of corporate social responsibility disclosure: An

application of stakeholder theory. Accounting, Organizations and Society, Vol. 17,

No.6, 595-612.

Rogers, J. (2008), 'Disclosure quality and management trading incentives', Journal of

Accounting Research, Vol. 46, No.5, pp. 1265-1296.

Rogers, R. and Grant, J. (1997), ‘Content analysis of information cited in reports of

sell-side financial analysts’, The Journal of Financial Statement Analysis, Vol.3 ,

No.1, pp. 17-30.

Roychowdhury, S. and Sletten, E. (2012),'Voluntary disclosure incentives and earnings

informativeness', The Accounting Review, Vol. 87, No. 5, pp. 1679-1708.

Saunders, M., Lewis, P. and Thornhill, A. (2009), ‘Research Methods for Business

Students’, 5th edition, Prentice Hall, London, UK.

Schadewitz, H. and Blevins, D. (1998), ‘Major determinants of interim disclosures in an

emerging market’, American Business Review, Vol.16, No.1, pp. 41–55.

Schipper, K. (1991), ‘Analysts’ forecasts’, Accounting Horizons, Vol. 5, No. 4, pp.105-

121.

Schleicher, T., Hussainey, K. and Walker, M. (2007), ‘Loss firms' annual report

narratives and share price anticipation of earnings’, The British Accounting Review,

Vol. 39, No.2, pp.153-171.

REFERENCES

Ap

pen

dic

es

239

Setia-Atmaja, L. (2009), ‘Governance Mechanisms and Firm Value: The Impact of

Ownership Concentration and Dividends’, Corporate Governance: An

International Review, Vol.17, No.6, pp. 694–709.

Skinner, D. (Spring, 1994), ‘Why firms voluntarily disclose bad news’, Journal of

Accounting Research, Vol.32, No.1, pp.38-60.

Shleifer, A. and Vishny, R. (1997), ‘A survey of corporate governance’, Journal of

Finance, Vol. 52, No. 2, pp.737-783.

Singh, M., Mathur, I. and Gleason, K. (2004), ‘Governance and performance

implications of diversification strategies: evidence from large US Firms’, Financial

Review, Vol. 39, pp. 489-526.

Singhvi, S., and Desai, H. (1971), ‘An empirical analysis of the quality of corporate

financial disclosure’, The Accounting Review, Vol. 46, No. 1, pp. 129- 138.

Sievers, S., Mokwa, C., and Keienburg, G. (2013), ' The relevance of financial versus

non-financial information for the valuation of venture capital-backed firms',

European Accounting Review, Vol. 22, No. 3, pp.467-513.

Smith Committee (2003), ‘Audit committee combined code guidance’, Financial

Reporting Council, London, Available at:

http://www.ecgi.org/codes/documents/ac_report.pdf

Solomon, J. (2010), ‘Corporate governance and accountability’, 3rd edition, UK: John

Wiley& Sons Ltd.

Solomon, J., Solomon, A., Norton, S., and Joseph, N. (2000), 'A conceptual framework

for corporate risk disclosure emerging from the agenda for corporate governance

reform', The British Accounting Review, Vol. 32, No.4, pp.447-478.

Spence, M. (1973), ‘Job market signalling’, Quarterly Journal of Economics, Vol. 87,

No.1, pp. 355-374.

Tabachnick, B. and Fidell, L. (2007), ‘Using multivariate statistics’, fifth edition, Pearson Education, Inc., London.

Tauringana, V. and Mangena, M. (2009), ‘The Influence of the Business Review on Reporting Key Performance Indicators in the UK Media Sector’, The Institute of

Chartered Accountants of Scotland, Edinburgh, available at: http://www.icas.org.uk/site/cms/download/res/Tauringana_report_KPI_Nov_09.pdf

The Holy Qur’an.

Tsalavoutas, I. (2009), ‘The adoption of IFRS by Greek listed companies: financial statement effects, level of compliance and value relevance’, PhD Thesis, The

University of Edinburgh, UK. Tsalavoutas, I., Evans, L. and Smith, M. (2010), 'Comparison of two methods for

measuring compliance with IFRS mandatory disclosure requirements', Journal of

Applied Accounting Research, Vol. 11, No. 3,pp. 213-228.

Tsalavoutas, I., André, P., Evans, L. (2012), ‘The transition to IFRS and the value

relevance of financial statements in Greece’, The British Accounting Review, Vol.

44, pp. 262–277.

Ujunwa, A. (2012), ‘Board characteristics and the financial performance of Nigerian

quoted firms’, Corporate Governance, Vol. 12, No. 5, pp. 656 – 674.

http://www.ecgi.org/codes/documents/ac_report.pdf

http://www.icas.org.uk/site/cms/download/res/Tauringana_report_KPIs_Nov_09.pdf

REFERENCES

Ap

pen

dic

es

240

Ullmann, A. (1985). Data in search of a theory: A critical examination of the

relationship among social performance, social disclosure and economic

performance. Academy of Management Review, 10:3, 540-577.

Urquiza, F., Navarro, M and Trombetta, M. (2009), “Disclosure indices design: does it

make a difference?’’, Spanish Accounting Review, Vol.12, No.2, pp. 253-278.

Uyar, A., and Kiliç, M. (2012),' Value relevance of voluntary disclosure: evidence from

Turkish firms', Journal of Intellectual Capital, Vol.13, No.3, pp.363-376.

Vanstraelen, A., Zarzeski, M., and Robb, S. (2003), ‘Corporate nonfinancial disclosure

practices and financial analyst forecast ability across three European Countries’,

Journal of International Financial Management and Accounting, Vol.14,, No. 249–

278.

Verrecchia, R. (2001), ‘Essays of disclosure’, Journal of Accounting and Economics,

Vol.32, No.1, pp.97-180.

Vafeas, N. and E. Theodorou (1998), ‘The Relationship between board structure and firm performance in the UK’, British Accounting Review, Vol. 30, pp. 383–407.

Wallace, R., Nasser, K. and Mora, A. (1994), ‘The relationship between the

comprehensiveness of corporate annual reports and firm characteristics in Spain’,

Accounting and Business Research, Vol. 25, No.97, pp. 41-53.

Wang, K., Sewon, O. and Claiborne, C. (2008), ‘Determinants and consequences of

voluntary disclosure in an emerging market: evidence from China’, Journal of

International Accounting, Auditing and Taxation, Vol.17, No.1, pp.14-30.

Wang, M. and Hussainey, K. (2013), 'Voluntary forward-looking statements driven by

corporate governance and their value relevance', Journal of Accounting and Public

Policy, Vol. 32, no. 3, pp. 26-49.

Watts, R. and Zimmerman, J. (1978), ‘Towards a positive theory of the determinant of

accounting standards’, The Accounting Review, Vol.53, No.1, PP.112-134.

Watts, R. and Zimmerman, J. (1990), ‘Positive accounting theory: a ten year

perspective’, The Accounting Review, Vol.65, No. 1, pp.131-156.

Watson, A., Shrives, P. and Marston, C. (2002), ‘Voluntary disclosure of accounting

ratios in the UK’, British Accounting Review, Vol. 34, pp. 289-313.

Weetman, P., Collins, B. And Davie, E. (1994), Operating and Financial Review:

Views of Analysts and Institutional Investors’, Research Report, The Institute of

Chartered Accountants of Scotland.

Weir, C., Laing, D. and Phillip, M.(2002), 'Internal and external governance

mechanisms: Their impact on the performance of large UK public companies',

Journal of Business Finance & Accounting, Vol. 29 No.5/6, pp.579-612.

Welker, M. (1995), ‘Disclosure policy, information asymmetry and liquidity in equity

markets’, Contemporary Accounting Research, Vol.11, No.2, pp. 801-827.

Wong, J., (1988), ‘ Political costs and intra-period accounting choice for export tax

credits’, Journal of Accounting and Economics , Vol. 10, No. 1, pp. 37–51.

Wooldridge, J. (2003), ‘Introductory econometrics: A modern approach’, Second edition, South-Western College Pub., US.

World Intellectual Capital Initiative (WICI) (2010), “RE: IASB Exposure Draft

ED/2009/6 Management Commentary, available at: www.worldici.com

http://www.worldici.com/

REFERENCES

Ap

pen

dic

es

241

Yermack, D. (1996), ‘Higher Market Valuation of Companies with a Small Board of

Directors’, Journal of Financial Economics, Vol. 40, pp. 185–211.

Appendices

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Appendix 1

Table 41 Anova test to compare financial KPI reporting quantity across sample

period

QNFKS: is the numberof financial KPIs disclosed in the KPI’ section.

0.000 0.382 1.000 1.000 2010 .606977 .277262 .121886 .068075 0.001 1.000 1.000 2009 .538903 .209188 .053812 0.004 1.000 2008 .485091 .155376 0.153 2007 .329715 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QNFKS by year


Total 472.548176 502 .941331027 Within groups 449.617798 498 .902846985Between groups 22.9303773 4 5.73259432 6.35 0.0001 Source SS df MS F Prob > F Analysis of Variance

Total 2.0979776 .97022215 503 2010 2.308016 .81160632 102 2009 2.2399414 .85659085 102 2008 2.1861296 .90325218 102 2007 2.0307538 1.0312948 101 2006 1.7010386 1.1249368 96 year Mean Std. Dev. Freq. Summary of QNFKS

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Table 42 Anova test to compare non-financial KPI reporting quantity across

sample period

QNNFKSEC: is the numberof non-financial KPIs disclosed in the KPI’ section.

0.002 0.292 1.000 1.000 2010 .532508 .307766 .152718 .063843 0.011 0.836 1.000 2009 .468665 .243923 .088875 0.080 1.000 2008 .37979 .155048 1.000 2007 .224742 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QNNFKSEC by year



Total 1.0652193 1.0160555 503 2010 1.272564 .99910395 102 2009 1.208721 1.0291649 102 2008 1.1198459 1.0344857 102 2007 .96479811 1.0049152 101 2006 .74005562 .9370861 96 year Mean Std. Dev. Freq. Summary of QNNFKSEC

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sample period

QNNFKREP: is the numberof non-financial KPIs disclosed in the whole report.

0.000 0.002 0.237 1.000 2010 .790697 .546945 .335523 .164574 0.000 0.102 1.000 2009 .626124 .382371 .170949 0.026 1.000 2008 .455174 .211422 1.000 2007 .243752 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QNNFKREP by year



Total 1.2982137 1.0877437 503 2010 1.6603572 1.0751321 102 2009 1.4957837 1.0890472 102 2008 1.3248344 1.0712718 102 2007 1.1134123 1.0328102 101 2006 .86966009 1.0069933 96 year Mean Std. Dev. Freq. Summary of QNNFKREP

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Table 44 Anova test to compare total KPI reporting quantity across sample period

QNTKREP: is the total number of financial and non-financial KPIs disclosed in the whole report.

0.000 0.004 0.610 1.000 2010 .935704 .555817 .291154 .140025 0.000 0.077 1.000 2009 .795678 .415792 .151129 0.000 0.893 2008 .64455 .264663 0.165 2007 .379886 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QNTKREP by year



Total 2.6180589 1.1509722 503 2010 2.9956839 .9625605 102 2009 2.8556587 1.0105928 102 2008 2.7045298 1.0710509 102 2007 2.4398667 1.2070279 101 2006 2.0599803 1.267246 96 year Mean Std. Dev. Freq. Summary of QNTKREP

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Table 45 Anova test to compare financial KPI reporting quality across sample

period

QLFKS: is the quality of financial KPIs disclosed in the KPI’ section.

0.000 0.030 1.000 1.000 2010 .176605 .092419 .04632 .032043 0.000 0.516 1.000 2009 .144561 .060376 .014276 0.000 1.000 2008 .130285 .0461 0.076 2007 .084185 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QLFKS by year



Total .54299186 .22775073 503 2010 .61114569 .1685289 102 2009 .57910255 .19790911 102 2008 .56482616 .20792088 102 2007 .51872639 .24069706 101 2006 .43454118 .2755 96 year Mean Std. Dev. Freq. Summary of QLFKS

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Table 46 Anova test to compare non-financial KPI reporting quality across sample

period

QLNFKS EC: is the quality of non- financial KPIs disclosed in the KPI’ section.

0.002 0.457 1.000 1.000 2010 .175096 .093202 .056601 .034686 0.030 1.000 1.000 2009 .14041 .058516 .021915 0.123 1.000 2008 .118495 .036601 0.837 2007 .081894 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QLNFKSEC by year



Total .39406279 .33541326 503 2010 .46470652 .32394336 102 2009 .43002065 .32808533 102 2008 .40810578 .33626389 102 2007 .3715047 .33941936 101 2006 .28961094 .3293495 96 year Mean Std. Dev. Freq. Summary of QLNFKSEC

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Table 47 Anova test to compare total non-financial KPI reporting quality across

sample period

QLNFKREP: is the quality of non financial KPIs disclosed in the whole report.

0.000 0.065 0.822 1.000 2010 .214053 .123866 .078804 .043222 0.002 0.761 1.000 2009 .170831 .080644 .035582 0.034 1.000 2008 .135249 .045062 0.508 2007 .090187 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QLNFKREP by year



Total .44796885 .32998493 503 2010 .53843836 .30214305 102 2009 .49521657 .31681598 102 2008 .45963429 .32755073 102 2007 .41457245 .3360613 101 2006 .32438554 .3327659 96 year Mean Std. Dev. Freq. Summary of QLNFKREP

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Table 48 Anova test to compare total KPI reporting quality across sample period

QLTKREP: is the aggregated quality of financial and non-financial KPIs disclosed in the whole report.

0.000 0.016 1.000 1.000 2010 .167191 .092168 .040172 .026949 0.000 0.254 1.000 2009 .140242 .065219 .013223 0.000 0.745 2008 .127019 .051996 0.114 2007 .075023 Col Mean 2006 2007 2008 2009Row Mean- (Bonferroni) Comparison of QLTKREP by year



Total .57008805 .21454021 503 2010 .63411513 .15538007 102 2009 .60716629 .17814543 102 2008 .59394324 .18671721 102 2007 .54194692 .23430768 101 2006 .46692433 .26621597 96 year Mean Std. Dev. Freq. Summary of QLTKREP

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Table 49 Anova test to compare financial KPI reporting quantity across industries

QNFKS: is the numberof financial KPIs disclosed in the KPI’ section.

Rows: Consumer Goods, Consumer Services, Health Care, Industrials, Oil & Gas, Technology,

Telecommunications, Utilities.

Columns: Basic materials, Consumer Goods, Consumer Services, Health Care, Industrials, Oil &

Gas, Technology, Telecommunications.

1.000 1.000Utilitie .344898 .23108 1.000Telecomm .113818 Col Mean Technolo TelecommRow Mean-

1.000 1.000 1.000 0.024 1.000 0.099Utilitie .532714 .230799 .332246 .989271 .372007 .752786 1.000 1.000 1.000 1.000 1.000 1.000Telecomm .301634 -.000282 .101165 .758191 .140926 .521706 1.000 1.000 1.000 0.333 1.000 1.000Technolo .187816 -.114099 -.012653 .644373 .027109 .407888 1.000 0.113 0.310 1.000 0.464Oil & Ga -.220072 -.521987 -.420541 .236485 -.380779 1.000 1.000 1.000 0.128Industri .160708 -.141208 -.039761 .617264 1.000 0.034 0.088Health C -.456557 -.758473 -.657026 1.000 1.000Consumer .200469 -.101447 1.000Consumer .301916 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QNFKS by Industry


Total 472.548176 502 .941331027 Within groups 450.755958 494 .912461453Between groups 21.792218 8 2.72402725 2.99 0.0028 Source SS df MS F Prob > F Analysis of Variance

Total 2.0979776 .97022215 503 Utilities 2.5066405 .83776523 20Telecommunication 2.27556 .95558252 10 Technology 2.1617423 .90688077 40 Oil & Gas 1.7538544 1.1565705 54 Industrials 2.1346336 .90537442 143 Health Care 1.5173691 .74431542 24Consumer Services 2.174395 1.0755071 107 Consumer Goods 2.2758417 .87798965 65 Basic Materials 1.973926 .8029555 40 Industry Mean Std. Dev. Freq. Summary of QNFKS

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Table 50 Anova test to compare non-financial KPI reporting quantity across

industries

QNNFKSEC: is the numberof non-financial KPIs disclosed in the KPI’ section.





0.000 0.000Utilitie 2.3836 1.81194 1.000Telecomm .571659 Col Mean Technolo TelecommRow Mean-

0.000 0.000 0.000 0.000 0.000 0.000Utilitie 1.48739 2.01011 1.68641 2.26013 1.73378 1.64355 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.324547 .198178 -.125525 .448193 -.078158 -.168391 0.001 1.000 0.002 1.000 0.004 0.005Technolo -.896206 -.373481 -.697184 -.123466 -.649817 -.74005 1.000 1.000 1.000 0.254 1.000Oil & Ga -.156156 .366569 .042866 .616584 .090233 1.000 1.000 1.000 0.379Industri -.246389 .276336 -.047367 .526351 0.049 1.000 0.233Health C -.77274 -.250015 -.573717 1.000 0.979Consumer -.199022 .323702 0.191Consumer -.522725 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QNNFKSEC by Industry


Total 518.249114 502 1.03236875 Within groups 426.473831 494 .86330735Between groups 91.7752828 8 11.4719103 13.29 0.0000 Source SS df MS F Prob > F Analysis of Variance

Total 1.0652193 1.0160555 503 Utilities 2.8047565 1.0429371 20Telecommunication .99281998 .89088426 10 Technology .42116074 .69623409 40 Oil & Gas 1.1612109 .89216394 54 Industrials 1.0709777 .93815756 143 Health Care .54462708 .74836347 24Consumer Services 1.1183446 .92234103 107 Consumer Goods .79464214 .86562251 65 Basic Materials 1.317367 1.2565892 40 Industry Mean Std. Dev. Freq. Summary of QNNFKSEC

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industries

QNNFKREP: is the numberof non-financial KPIs disclosed in the whole report.






0.000 0.000 0.000 0.000 0.000 0.000Utilitie 1.58005 1.96102 1.68304 1.75446 1.65404 1.7811 1.000 1.000 1.000 1.000 1.000 1.000Telecomm .124104 .505077 .2271 .298512 .198094 .325151 0.355 1.000 0.362 1.000 0.172 1.000Technolo -.594768 -.213795 -.491771 -.420359 -.520778 -.393721 1.000 1.000 1.000 1.000 1.000Oil & Ga -.201047 .179926 -.09805 -.026639 -.127057 1.000 1.000 1.000 1.000Industri -.07399 .306984 .029007 .100419 1.000 1.000 1.000Health C -.174408 .206565 -.071412 1.000 1.000Consumer -.102996 .277977 1.000Consumer -.380973 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QNNFKREP by Industry



Total 1.2982137 1.0877437 503 Utilities 2.9823485 .85621959 20Telecommunication 1.526404 1.5881824 10 Technology .80753223 1.0242654 40 Oil & Gas 1.2012531 .93240813 54 Industrials 1.3283105 1.005718 143 Health Care 1.2278917 1.1761164 24Consumer Services 1.2993035 .94351609 107 Consumer Goods 1.0213269 1.0015459 65 Basic Materials 1.4023 1.2738112 40 Industry Mean Std. Dev. Freq. Summary of QNNFKREP

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Table 52 Anova test to compare KPI reporting quantity across industries

QNTKSEC: is the number of financial and non-financial KPIs disclosed in the the KPI’section.






0.003 0.000 0.000 0.000 0.000 0.000Utilitie 1.16945 1.22505 1.17476 2.00503 1.20018 1.61642 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.022508 .03309 -.017196 .813075 .008222 .42446 1.000 1.000 1.000 1.000 1.000 1.000Technolo -.234036 -.178438 -.228725 .601546 -.203306 .212932 1.000 1.000 0.520 1.000 0.575Oil & Ga -.446968 -.39137 -.441656 .388614 -.416238 1.000 1.000 1.000 0.028Industri -.03073 .024868 -.025418 .804852 0.101 0.093 0.025Health C -.835582 -.779985 -.830271 1.000 1.000Consumer -.005311 .050286 1.000Consumer -.055598 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QNTKSEC by Industry



Total 2.4966587 1.1187863 503 Utilities 3.7435715 1.1376884 20Telecommunication 2.551615 1.1495194 10 Technology 2.3400865 .81586733 40 Oil & Gas 2.1271547 1.4252527 54 Industrials 2.5433927 .96398247 143 Health Care 1.7385404 .81984871 24Consumer Services 2.5688111 1.1848418 107 Consumer Goods 2.5185249 .98984638 65 Basic Materials 2.5741225 1.0729199 40 Industry Mean Std. Dev. Freq. Summary of QNTKSEC

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Table 53 Anova test to compare total KPI reporting quantity across industries

QNTKREP: is the numberof financial and non-financial KPIs disclosed in the whole report.





0.000 0.863Utilitie 1.39493 .976399 1.000Telecomm .418531 Col Mean Technolo TelecommRow Mean-

0.001 0.000 0.000 0.000 0.000 0.000Utilitie 1.27683 1.27341 1.22748 1.78562 1.20184 1.73669 1.000 1.000 1.000 1.000 1.000 1.000Telecomm .300434 .297008 .251082 .809223 .22544 .760293 1.000 1.000 1.000 1.000 1.000 1.000Technolo -.118097 -.121523 -.167449 .390693 -.193091 .341762 1.000 0.873 0.229 1.000 0.100Oil & Ga -.459859 -.463285 -.509211 .048931 -.534853 1.000 1.000 1.000 0.642Industri .074994 .071568 .025642 .583784 1.000 1.000 0.968Health C -.508789 -.512216 -.558142 1.000 1.000Consumer .049352 .045926 1.000Consumer .003426 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QNTKREP by Industry



Total 2.6180589 1.1509722 503 Utilities 3.8889245 .83993432 20Telecommunication 2.912526 1.5337671 10 Technology 2.4939952 .9766443 40 Oil & Gas 2.1522334 1.4463092 54 Industrials 2.6870862 .96223914 143 Health Care 2.1033025 1.1387787 24Consumer Services 2.661444 1.1814895 107 Consumer Goods 2.6155183 1.070477 65 Basic Materials 2.612092 1.0979982 40 Industry Mean Std. Dev. Freq. Summary of QNTKREP

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Table 54 Anova test to compare financial KPI reporting quality across industries

QLFKS: is the quality of financial KPIs disclosed in the KPI’ section.






1.000 1.000 1.000 1.000 1.000 0.681Utilitie -.041115 .024423 .064563 .045206 .026331 .138878 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.046338 .019201 .059341 .039983 .021109 .133655 1.000 1.000 1.000 1.000 1.000 1.000Technolo -.105026 -.039487 .000653 -.018704 -.037579 .074967 0.005 0.217 1.000 1.000 0.067Oil & Ga -.179993 -.114455 -.074314 -.093672 -.112547 1.000 1.000 1.000 1.000Industri -.067446 -.001908 .038232 .018875 1.000 1.000 1.000Health C -.086321 -.020783 .019357 0.421 1.000Consumer -.105678 -.04014 1.000Consumer -.065538 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QLFKS by Industry


Total 26.0389385 502 .051870395 Within groups 25.0788179 494 .050766838Between groups .960120561 8 .12001507 2.36 0.0167 Source SS df MS F Prob > F Analysis of Variance

Total .54299186 .22775073 503 Utilities .58635065 .16717525 20Telecommunication .5811282 .26060354 10 Technology .52244032 .21905217 40 Oil & Gas .44747301 .29989981 54 Industrials .56001952 .20450029 143 Health Care .54114475 .26520962 24Consumer Services .52178751 .22027122 107 Consumer Goods .56192752 .21609557 65 Basic Materials .62746595 .20321097 40 Industry Mean Std. Dev. Freq. Summary of QLFKS

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Table 55 Anova test to compare non-financial KPI reporting quality across

industries

QLNFKS EC: is the quality of non- financial KPIs disclosed in the KPI’ section.





Table 56 Anova test to compare total non-financial KPI reporting quality


1.000 0.012 0.249 0.001 0.206 1.000Utilitie .178198 .300295 .214431 .422329 .214998 .18454 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.09672 .025377 -.060487 .147411 -.05992 -.090378 0.007 0.740 0.003 1.000 0.002 0.003Technolo -.2736 -.151504 -.237368 -.02947 -.2368 -.267258 1.000 1.000 1.000 0.106 1.000Oil & Ga -.006342 .115755 .029891 .237789 .030458 1.000 1.000 1.000 0.142Industri -.0368 .085297 -.000567 .207331 0.134 1.000 0.171Health C -.244131 -.122034 -.207898 1.000 1.000Consumer -.036233 .085864 1.000Consumer -.122097 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QLNFKSEC by Industry



Total .39406279 .33541326 503 Utilities .63513256 .18565513 20Telecommunication .3602144 .38041737 10 Technology .18333413 .28765049 40 Oil & Gas .45059245 .308087 54 Industrials .42013434 .341711 143 Health Care .21280383 .28297486 24Consumer Services .42070183 .30735701 107 Consumer Goods .33483771 .34206707 65 Basic Materials .4569346 .38782524 40 Industry Mean Std. Dev. Freq. Summary of QLNFKSEC

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industries

QLNFKREP: is the quality of non financial KPIs disclosed in the whole report.





Table 57 Anova test to compare KPI reporting quality across industries


1.000 0.032 0.507 0.049 0.437 0.378Utilitie .19209 .277171 .194731 .31655 .195004 .218195 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.105029 -.019947 -.102388 .019432 -.102115 -.078924 0.372 1.000 0.083 1.000 0.059 0.648Technolo -.186765 -.101684 -.184124 -.062305 -.183851 -.16066 1.000 1.000 1.000 1.000 1.000Oil & Ga -.026105 .058976 -.023464 .098355 -.023192 1.000 1.000 1.000 1.000Industri -.002914 .082168 -.000272 .121547 1.000 1.000 1.000Health C -.12446 -.039379 -.121819 1.000 1.000Consumer -.002641 .08244 1.000Consumer -.085081 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QLNFKREP by Industry



Total .44796885 .32998493 503 Utilities .670487 .10975838 20Telecommunication .3733682 .39457427 10 Technology .291632 .3508671 40 Oil & Gas .45229182 .30948373 54 Industrials .47548339 .33356834 143 Health Care .35393666 .31060165 24Consumer Services .47575582 .29229158 107 Consumer Goods .39331554 .34491248 65 Basic Materials .47839703 .38589218 40 Industry Mean Std. Dev. Freq. Summary of QLNFKREP

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QLTKSEC: is the aggregated quality of financial and non-financial KPIs disclosed in the KPI’ section





Table 58 Anova test to compare total KPI reporting quality across industries


1.000 1.000 1.000 1.000 1.000 0.149Utilitie -.031333 .043534 .08038 .07206 .020649 .161965 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.068264 .006603 .043449 .035129 -.016282 .125034 1.000 1.000 1.000 1.000 1.000 0.713Technolo -.088597 -.01373 .023116 .014796 -.036616 .104701 0.001 0.104 0.837 1.000 0.002Oil & Ga -.193298 -.118431 -.081585 -.089905 -.141316 1.000 1.000 1.000 1.000Industri -.051982 .022885 .059731 .051411 1.000 1.000 1.000Health C -.103393 -.028526 .00832 0.187 1.000Consumer -.111713 -.036846 1.000Consumer -.074867 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QLTKSEC by Industry



Total .56171643 .21866956 503 Utilities .613934 .17850628 20Telecommunication .5770025 .25635769 10 Technology .55666935 .18279447 40 Oil & Gas .45196858 .29774119 54 Industrials .59328489 .17844292 143 Health Care .54187358 .26200302 24Consumer Services .53355371 .21027657 107 Consumer Goods .5703995 .21945245 65 Basic Materials .64526657 .20918528 40 Industry Mean Std. Dev. Freq. Summary of QLTKSEC

. oneway QLTKSEC Industry, t bon

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QLTKREP: is the aggregated quality of financial and non-financial KPIs disclosed in the whole report.





Appendix 2


1.000 1.000 1.000 1.000 1.000 0.020Utilitie -.001403 .076806 .093885 .115056 .040646 .191258 1.000 1.000 1.000 1.000 1.000 1.000Telecomm -.074724 .003485 .020564 .041735 -.032675 .117936 1.000 1.000 1.000 1.000 1.000 0.345Technolo -.078723 -.000514 .016565 .037736 -.036674 .113938 0.000 0.116 0.204 1.000 0.000Oil & Ga -.192661 -.114451 -.097373 -.076202 -.150612 1.000 1.000 1.000 1.000Industri -.042049 .03616 .053239 .07441 1.000 1.000 1.000Health C -.116459 -.03825 -.021171 0.529 1.000Consumer -.095288 -.017079 1.000Consumer -.078209 Col Mean Basic Ma Consumer Consumer Health C Industri Oil & GaRow Mean- (Bonferroni) Comparison of QLTKREP by Industry



Total .57008805 .21454021 503 Utilities .6450576 .10519763 20Telecommunication .57173629 .25095208 10 Technology .56773741 .1867596 40 Oil & Gas .45379989 .29890189 54 Industrials .60441149 .17808972 143 Health Care .53000158 .25248115 24Consumer Services .55117247 .19955523 107 Consumer Goods .56825123 .21693682 65 Basic Materials .64646052 .20931095 40 Industry Mean Std. Dev. Freq. Summary of QLTKREP

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Table 59 Determinants of financial KPI reporting quantity


EXCOMP 0.51**

0.233

0.44**

0.206

NOEXCOMP 0.462*

0.26

0.271 0.2

BORSIZE 0.06**

0.033

0.08**

0.038

BORCOMP 1.36**

0.534

0.973

0.61

BORMEET -0.022 0.021

-0.032 0.022

ROLEDUAL -0.362 0.221

-0.417 0.263

ACSIZE -0.024

0.084

-0.116

0.103

ACMEET 0.106 0.066

0.108 0.072


-0.183 0.338

MANGOWN 0.068

0.568

0.506

0.46

FUT_EQUITY 0.144 0.189

0.111 0.199

FUT_BONDS 0.111 0.117

0.075 0.076

FUT_LOANS 0.268**

0.109

0.22**

0.107

SIZE 0.225 0.138

0.37***

0.109

0.165 0.121

0.38***

0.105

0.46***

0.118

0.37***

0.099

-0.194 0.183

PROFITAB -1.35**

0.559

-1.17**

0.58

-1.16*

0.642

-1.122*

0.624

-1.30**

0.575

-1.077*

0.552

-0.881 0.617

LIQUIDITY 0.079

0.06

0.067

0.058

0.089*

0.052

0.069

0.056

0.07

0.059

0.073

0.058

0.084

0.057

LEVERAGE -0.289 0.273

-0.31 0.268

-0.233 0.244

-0.356 0.27

-0.27 0.263

-0.274 0.248

-0.318 0.254

DIVYIELD 1.805 2.143

1.687 2.203

1.526 1.96

1.828 2.256

2.1 2.184

1.73 2.192

0.524 2.151

CROSSLIST 0.279

0.234

0.245

0.241

0.23

0.228

0.347

0.237

0.273

0.233

0.223

0.233

0.266

0.264

Constant -3.60** 1.208

-4.07** 1.341

-1.224 0.9

-2.36** 0.98

-2.66** 1.123

-1.93** 0.933

-1.753 1.364

F 8.4*** 7.3*** 6.8*** 6.7*** 6.0*** 6.4*** 4.9***

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Adj R-squared 0.125 0.117 0.144 0.123 0.109 0.126 0.172

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


Dependent variables: QNFKS is the total number of financial KPIs disclosed in the KPI’ section.

Explanatory variables: executives’ compensations in Mo1; non-executives’ compensations in Mo2;

board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables

in Mo5; capital need variables in Mo6; and all explanatory variables used in the analyses in Mo7. All

variables are defined in Table 5 and Table 18. All regressions include industries dummies. Standard errors in the

second line for each variable and are corrected for firm and time clustering.

Table 60 Determinants of financial KPI reporting quality


EXCOMP 0.14**

0.067

0.108**

0.048

NOEXCOMP 0.127*

0.067

0.090*

0.054

BORSIZE 0.021**

0.007

0.024**

0.01

BORCOMP 0.42***

0.121

0.286**

0.121

BORMEET -0.002

0.003

-0.004

0.004

ROLEDUAL -0.16***

0.036

-0.2***

0.056

ACSIZE 0.01 0.021

-0.011 0.028

ACMEET 0.037**

0.014

0.036**

0.015


0.03 0.073

MANGOWN 0.037 0.151

0.222**

0.111

FUT_EQUITY -0.006

0.045

-0.018

0.042

FUT_BONDS -0.005 0.021

-0.021 0.015

FUT_LOANS 0.05***

0.015

0.032*

0.018

SIZE 0.041

0.032

0.1***

0.016

0.012

0.026

0.06***

0.019

0.11***

0.022

0.09***

0.02

-0.066*

0.038

PROFITAB -0.21*

0.114

-0.164

0.118

-0.143

0.13

-0.113

0.133

-0.208*

0.115

-0.170*

0.099

-0.081

0.122

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LIQUIDITY -0.002

0.015

-0.005

0.014

0.003

0.013

-0.004

0.014

-0.004

0.015

-0.003

0.015

0.001

0.012

LEVERAGE -0.098 0.075

-0.104 0.07

-0.086 0.06

-0.118 0.075

-0.091 0.072

-0.088 0.067

-0.098 0.067


0.553 0.682

0.456 0.643

0.537 0.693

0.715 0.694

0.638 0.654

0.278 0.692

CROSSLIST 0.037

0.067

0.027

0.069

0.021

0.064

0.06

0.065

0.034

0.066

0.027

0.067

0.04

0.067

Constant -0.6** 0.263

-0.72** 0.36

0.067 0.172

-0.206 0.182

-0.440** 0.216

-0.248 0.21

-0.394 0.345

F 8.8*** 8.4*** 9.6*** 9.5*** 6.5*** 6.0*** 6.6***

Adj R-squared 0.119 0.11 0.169 0.135 0.101 0.104 0.208

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


Dependent variables: QLFKS is the quality score of KPIs disclosed in the KPI’ section. Explanatory

variables: executives’’ compensations in Mo1; non-executives’ compensations in Mo2; board

characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5;

capital need variables in Mo6; and all explanatory variables used in the analyses in Mo7. All variables are

defined in Table 5 and Table 18. All regressions include industries dummies. Standard errors in the second line

for each variable and are corrected for firm and time clustering.

Table 61 Determinants of non-financial KPI reporting quantity


EXCOMP 0.091

0.311

-0.071

0.337

NOEXCOMP 0.666**

0.274

0.672**

0.282

BORSIZE 0.07**

0.026

0.10***

0.025

BORCOMP 0.86**

0.436

1.059**

0.431

BORMEET -0.04**

0.022

-0.038 0.025

ROLEDUAL -0.47**

0.221

-0.375 0.282

ACSIZE -0.021

0.08

-0.114

0.084

ACMEET -0.003 0.061

0.004 0.067


0.193 0.38

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MANGOWN -0.168

0.809

-0.018

0.885

FUT_EQUITY -0.126 0.077

-0.17*** 0.05

FUT_BONDS 0.119 0.107

0.084 0.089

FUT_LOANS 0.233*

0.12

0.193*

0.11

SIZE 0.203 0.175

0.124 0.116

-0.035 0.152

0.258*

0.145

0.27**

0.124

0.149 0.114

-0.191 0.162

PROFITAB -0.442 0.99

-0.257 0.977

-0.31 0.913

-0.465 1.033

-0.449 0.974

-0.245 0.992

-0.039 0.923

LIQUIDITY -0.008

0.066

-0.014

0.063

0.006

0.058

-0.009

0.066

-0.007

0.066

-0.007

0.068

0

0.055


0.017 0.236

0.161 0.207

0.067 0.233

0.066 0.235

0.08 0.205

0.091 0.201

DIVYIELD 2.304*

1.379

1.783 1.433

1.908 1.257

2.403 1.479

2.58**

1.262

2.376*

1.315

1.623*

0.957

CROSSLIST 0.185

0.193

0.144

0.203

0.153

0.186

0.182

0.206

0.181

0.189

0.155

0.197

0.087

0.216

Constant -1.326 1.375

-3.24** 1.563

0.609 1.278

-1.189 1.194

-1.558 1.238

-0.333 1.106

-0.878 1.819

F 7.3*** 7.5*** 6.5*** 6.0*** 5.8*** 6.2*** 4.8***

Adj R-squared 0.196 0.207 0.228 0.194 0.195 0.203 0.24

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498

***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. Dependent

variable: QNNFKSEC is the total number of non-financial KPIs disclosed in the KPI’ section. Explanatory


characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in

Mo5; capital need variables in Mo6; and all explanatory variables used in the analyses in Mo7. All


second line for each variable and are corrected for firm and time clus tering.

Table 62 Determinants of non-financial KPI reporting quality


EXCOMP 0.20**

0.079

0.125*

0.068

NOEXCOMP 0.24**

0.084

0.194**

0.062

BORSIZE 0.03**

0.01

0.037***

0.01

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BORCOMP 0.56**

0.179

0.427**

0.174

BORMEET -0.007 0.009

-0.008 0.01

ROLEDUAL -0.24**

0.072

-0.209**

0.092

ACSIZE 0.026

0.033

-0.014

0.035

ACMEET 0.027 0.021

0.026 0.022


0.071 0.118

MANGOWN -0.274

0.239

-0.072

0.261

FUT_EQUITY -0.022 0.045

-0.04 0.036

FUT_BONDS 0.026 0.038

-0.002 0.031

FUT_LOANS 0.055*

0.033

0.035

0.03

SIZE -0.002 0.051

0.043 0.033

-0.043 0.047

0.049 0.042

0.09**

0.04

0.065*

0.035

-0.15***

0.045

PROFITAB -0.186 0.237

-0.102 0.235

-0.092 0.21

-0.081 0.249

-0.149 0.23

-0.121 0.234

0.019 0.208

LIQUIDITY -0.012

0.019

-0.017

0.018

-0.006

0.016

-0.015

0.019

-0.012

0.018

-0.014

0.019

-0.006

0.015

LEVERAGE -0.082 0.086

-0.095 0.082

-0.058 0.077

-0.09 0.081

-0.087 0.081

-0.074 0.077

-0.09 0.08

DIVYIELD 0.89**

0.423

0.795*

0.441

0.721*

0.436

0.874

0.531

1.07**

0.422

0.99**

0.448

0.509 0.33

CROSSLIST 0.072

0.083

0.056

0.087

0.051

0.07

0.088

0.079

0.069

0.081

0.063

0.082

0.058

0.078

Constant -0.75* 0.384

-1.11** 0.488

0.253 0.367

-0.256 0.361

-0.429 0.405

-0.184 0.351

-0.476 0.501

F 5.1*** 6.0*** 6.4*** 4.6*** 4.8*** 4.3*** 4.1***

Adj R-squared 0.128 0.124 0.174 0.122 0.115 0.111 0.18

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


variable: QLNFKSEC is the quality score of non-financial KPIs disclosed in the KPI’ section. Explanatory


characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5;

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capital need variables in Mo6; and all explanatory variables used in the analyses in Mo7. All variables

are defined in Table 5 and Table 18. All regressions include industries dummies. Standard errors in the second

line for each variable and are corrected for firm and time clustering.

Table 63 The determinants of total non-financial KPI reporting quantity


EXCOMP 0.472*

0.281

0.154 0.333

NOEXCOMP 1.1***

0.288

0.91***

0.256

BORSIZE 0.039

0.026

0.066**

0.026

BORCOMP 1.1**

0.551

1.396**

0.507

BORMEET -0.024 0.024

-0.02 0.025

ROLEDUAL -0.69**

0.25

-0.507*

0.258

ACSIZE 0.002 0.078

-0.084 0.088

ACMEET 0.005 0.061

0.004 0.067

MAJORSHAR -0.552

0.395

-0.646*

0.386

MANGOWN -0.071 0.726

0.381 0.787

FUT_EQUITY -0.221 0.154

-0.241*

0.129

FUT_BONDS 0.092

0.08

0.048

0.069

FUT_LOANS 0.321*

0.17

0.269 0.165

SIZE 0.219 0.181

0.232*

0.131

0.217 0.159

0.42**

0.147

0.35**

0.138

0.32**

0.136

-0.216 0.17

PROFITAB -0.799

0.941

-0.459

0.924

-0.687

0.858

-0.736

0.979

-0.671

0.966

-0.523

0.949

-0.26

0.944

LIQUIDITY -0.013 0.067

-0.028 0.063

0.003 0.061

-0.02 0.065

-0.02 0.062

-0.015 0.067

-0.005 0.057

LEVERAGE -0.05 0.251

-0.123 0.245

0.014 0.239

-0.039 0.222

-0.048 0.231

-0.008 0.219

-0.057 0.204

DIVYIELD 1.913

1.349

1.228

1.476

1.763

1.224

2.152

1.431

1.685

1.384

2.257

1.466

0.448

1.202


-0.141 0.21

-0.109 0.225

-0.071 0.234

-0.069 0.213

-0.112 0.222

-0.19 0.235

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Constant -3.4**

1.514

-5.95***

1.634

-1.376

1.356

-2.489*

1.31

-1.497

1.394

-1.554

1.297

-2.61

1.805

F 8.3*** 10.9*** 7.0*** 7.3*** 8.2*** 7.7*** 5.5***

Adj R-squared 0.184 0.202 0.201 0.173 0.178 0.187 0.233

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


variable: QNNFKREP is the number of non financial KPIs disclosed in the whole report. Explanatory






Table 64 The determinants of total non-financial KPI reporting quality


EXCOMP 0.304***

0.063

0.180**

0.061

NOEXCOMP 0.33***

0.075

0.24***

0.044

BORSIZE 0.019**

0.009

0.026**

0.009

BORCOMP 0.711***

0.171

0.623***

0.168

BORMEET -0.002 0.009

-0.004 0.009

ROLEDUAL -0.26***

0.07

-0.204**

0.074

ACSIZE 0.025

0.029

-0.015

0.031

ACMEET 0.030*

0.017

0.025 0.018

MAJORSHAR -0.096 0.124

-0.157 0.113

MANGOWN -0.243

0.223

0.024

0.235

FUT_EQUITY -0.034 0.06

-0.05 0.054

FUT_BONDS 0.018 0.026

-0.011 0.02

FUT_LOANS 0.078*

0.046

0.054

0.043

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SIZE -0.004

0.049

0.071*

0.037

0.022

0.049

0.09**

0.042

0.11**

0.042

0.11**

0.042

-0.155**

0.047

PROFITAB -0.261 0.226

-0.14 0.224

-0.186 0.19

-0.14 0.235

-0.191 0.228

-0.175 0.218

-0.049 0.188

LIQUIDITY -0.009 0.02

-0.016 0.019

-0.002 0.016

-0.013 0.019

-0.011 0.018

-0.013 0.02

-0.003 0.016

LEVERAGE -0.087

0.089

-0.104

0.085

-0.075

0.086

-0.094

0.077

-0.09

0.08

-0.071

0.079

-0.106

0.073


0.515 0.527

0.552 0.473

0.67 0.564

0.702 0.508

0.796 0.534

0.084 0.443


-0.032 0.07

-0.033 0.057

0.008 0.069

-0.01 0.071

-0.022 0.07

-0.03 0.058

Constant -1.272**

0.419

-1.73***

0.48

-0.276

0.395

-0.573

0.38

-0.422

0.432

-0.483

0.406

-0.851*

0.501

F 7.8*** 8.6*** 7.9*** 6.0*** 6.3*** 5.3*** 5.7***

Adj R-squared 0.146 0.131 0.178 0.116 0.11 0.108 0.216

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


variable: QLNFKREP is the quality score of non financial KPIs disclosed in the whole report. Explanatory






Table 65 The determinants of total KPI reporting quantity


EXCOMP 0.74**

0.305

0.52**

0.258

NOEXCOMP 1.023**

0.324

0.75**

0.276

BORSIZE 0.08**

0.034

0.11**

0.038

BORCOMP 1.66**

0.604

1.42**

0.548

BORMEET -0.029 0.02

-0.03*

0.021

ROLEDUAL -0.7**

0.242

-0.7**

0.269

ACSIZE -0.009

0.096

-0.13

0.113

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ACMEET 0.091

0.069

0.094

0.076

MAJORSHAR -0.294 0.336

-0.458 0.315

MANGOWN 0.034 0.73

0.692 0.605

FUT_EQUITY -0.019

0.238

-0.051

0.225

FUT_BONDS 0.167**

0.082

0.113 .

FUT_LOANS 0.383**

0.159

0.311*

0.159

SIZE 0.277

0.175

0.420**

0.137

0.252*

0.14

0.53***

0.128

0.56***

0.135

0.47***

0.125

-0.32*

0.18

PROFITAB -1.25*

0.708

-0.904 0.693

-1.02*

0.602

-1.011 0.752

-1.141 0.71

-0.87 0.648

-0.542 0.581


0.05 0.073

0.086 0.062

0.057 0.07

0.057 0.071

0.062 0.071

0.076 0.065

LEVERAGE -0.222

0.298

-0.281

0.293

-0.139

0.241

-0.268

0.261

-0.203

0.272

-0.189

0.245

-0.251

0.222

DIVYIELD 2.55 2.464

2.073 2.639

2.246 2.201

2.708 2.576

2.697 2.579

2.738 2.429

0.688 2.136

CROSSLIST 0.11 0.25

0.04 0.261

0.049 0.257

0.166 0.261

0.105 0.254

0.043 0.256

0.039 0.297

Constant -4.7**

1.549

-6.5***

1.571

-1.509

1.138

-3.03**

1.179

-2.76**

1.22

-2.13*

1.136

-2.9**

1.468

F 10.9*** 11.7*** 9.2*** 9.0*** 8.8*** 9.1*** 6.8***

Adj R-squared 0.177 0.178 0.201 0.161 0.155 0.172 0.241

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

N 498 498 498 498 498 498 498


variables: QNTKREP is the total number of financial and non-financial KPIs disclosed in the whole report. All

variables are defined in

Table 20. Explanatory variables: executives’’ compensations in Mo1; non-executives’ compensations in Mo2;

board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in




Table 66 The determinants of total KPI reporting quality


EXCOMP 0.2***

0.046

0.16***

0.034

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NOEXCOMP 0.2***

0.05

0.15***

0.038

BORSIZE 0.019**

0.006

0.023**

0.009

BORCOMP 0.48***

0.106

0.308**

0.109

BORMEET 0.0001

0.003

-0.001

0.004

ROLEDUAL -0.2***

0.037

-0.12***

0.055

ACSIZE 0.023 0.021

-0.001 0.027

ACMEET 0.035***

0.01

0.033**

0.011


0.022 0.057

MANGOWN -0.034 0.151

0.186*

0.101

FUT_EQUITY -0.018

0.047

-0.03

0.044

FUT_BONDS 0.01 0.007

-0.01 0.011

FUT_LOANS 0.038**

0.013

0.019 0.016

SIZE 0.013

0.026

0.07***

0.017

0.016

0.022

0.06***

0.017

0.1***

0.021

0.1***

0.021

-0.1**

0.036

PROFITAB -0.146 0.14

-0.072 0.141

-0.071 0.114

-0.028 0.147

-0.125 0.137

-0.097 0.129

0.011 0.102


-0.001 0.014

0.008 0.011

0.001 0.012

0.001 0.014

0.001 0.014

0.006 0.011

LEVERAGE -0.07

0.063

-0.079

0.059

-0.061

0.045

-0.084

0.06

-0.065

0.058

-0.061

0.054

-0.079

0.056


0.534 0.673

0.487 0.593

0.561 0.664

0.734 0.673

0.704 0.644

0.242 0.578


-0.021 0.058

-0.025 0.052

0.014 0.051

-0.01 0.056

-0.014 0.056

-0.007 0.051

Constant -0.7**

0.226

-0.95**

0.298

0.018

0.149

-0.201

0.17

-0.38*

0.211

-0.223

0.21

-0.68**

0.263

F 10.5*** 9.4*** 10.6*** 10.0*** 6.0*** 5.6*** 8.1***

Adj R-squared 0.169 0.145 0.211 0.164 0.121 0.123 0.273

Mean VIF 1.59 1.31 1.42 1.27 1.29 1.29 1.79

Max VIF 2.61 1.64 2.62 1.55 1.49 1.73 5.18

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N 498 498 498 498 498 498 498


variable: QLTKREP is the aggregated quality score of financial and non-financial KPIs disclosed in the whole

report. Explanatory variables: executives’’ compensations in Mo1; non-executives’ compensations in Mo2;

board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in


variables are defined in Table 5 and Table 18. All regressions include industries dummies. Standard errors in

the second line for each variable and are corrected for firm and time clustering.

Appendix 3

Table 67 Firm value (TQ) & total non-financial KPI reporting quantity


QNNFKREP -0.017 0.012

-0.017 0.013

-0.012 0.012

-0.016 0.013

-0.015 0.012

-0.012 0.011

EXCOMP 0.056 0.061

0.047 0.06

NOEXCOMP 0.046

0.078

0.059

0.058

BORSIZE -0.012 0.008

-0.016**

0.007

BORCOMP -0.046 0.125

-0.217 0.132

BORMEET -0.007

0.008

-0.008

0.007

ROLEDUAL 0.149**

0.048

0.170**

0.059

ACSIZE 0.014 0.017

0.027 0.02

ACMEET 0.007

0.017

0.013

0.012

MANGOWN -0.14 0.181

-0.212 0.145


0.111 0.116

SIZE 0.045

0.029

0.063**

0.022

0.103***

0.026

0.057**

0.024

0.077**

0.026

0.074**

0.033

PROFITAB 0.896**

0.299

0.912**

0.31

0.853**

0.309

0.934**

0.284

0.904**

0.3

0.909**

0.286


0.073 0.086

0.079 0.06

0.076 0.071

0.076 0.079

0.059 0.064

CROSSLIST -0.011

0.041

-0.015

0.042

-0.012

0.044

-0.007

0.046

-0.012

0.041

0.001

0.043

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CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003 0.003

0.003 0.003

0.002 0.003

0.004 0.003

0.003 0.003

0.003 0.004

PRPLEQ_SALES 0.0001

0

0.0001

0

0.0001

0

0.0001

0

0.0001

0

0.0001

0

Constant -0.65** 0.25

-0.71** 0.342

-0.661** 0.223

-0.52** 0.198

-0.65** 0.273

-1.01*** 0.268

F 11.7*** 11.6*** 11.3*** 11.1*** 11.2*** 9.67***

Adj R-squared 0.273 0.272 0.299 0.273 0.276 0.318

Mean VIF 2.05 1.9 1.91 1.86 1.88 2.16

Max VIF 3.89 3.9 3.89 3.9 4.09 5.52

N 485 485 485 485 485 485


Dependent variable: TQ +3: is TobinsQ three months after the year end. Explanatory variables: : Q NNFKREP is the quantity of non-financial KPIs disclosed in the whole report in addition to executives’’ compensations in Mo1; non-executives’ compensations in Mo2;

board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.

Table 68 Firm value (TQ) & total non-financial KPI reporting quality


QLNFKREP -0.082**

0.041

-0.075*

0.044

-0.053

0.047

-0.076

0.048

-0.072*

0.043

-0.068*

0.038

EXCOMP 0.074 0.059

0.057 0.058


0.064 0.056

BORSIZE -0.012

0.008

-0.015**

0.007

BORCOMP -0.019 0.13

-0.19 0.132

BORMEET -0.006 0.008

-0.008 0.007

ROLEDUAL 0.146**

0.047

0.167**

0.058

ACSIZE 0.016 0.017

0.027 0.019

ACMEET 0.009 0.018

0.014 0.012

MANGOWN -0.159

0.177

-0.22

0.144

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MAJORSHAR 0.118

0.12

0.109

0.111

SIZE 0.041 0.028

0.065**

0.023

0.101***

0.027

0.058**

0.024

0.081**

0.027

0.067**

0.031

PROFITAB 0.875**

0.298

0.899**

0.309

0.844**

0.311

0.922**

0.284

0.888**

0.299

0.894**

0.288

LEVERAGE 0.066

0.076

0.062

0.083

0.071

0.058

0.064

0.068

0.065

0.076

0.046

0.061


-0.015 0.042

-0.012 0.043

-0.005 0.046

-0.011 0.041

0.002 0.043

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003

0.003

0.003

0.003

0.003

0.003

0.004

0.003

0.003

0.004

0.003

0.004


0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

Constant -0.704** 0.255

-0.745** 0.334

-0.663** 0.224

-0.537** 0.2

-0.664** 0.281

-1.045*** 0.274

F 12.1*** 11.9*** 11.4*** 11.4*** 11.5*** 9.9***

Adj R-squared 0.28 0.276 0.301 0.279 0.281 0.323

Mean VIF 2.05 1.89 1.91 1.84 1.87 2.16

Max VIF 3.9 3.92 3.9 3.92 4.09 5.57

N 485 485 485 485 485 485


Dependent variable: TQ +3: is TobinsQ three months after the year end. Explanatory variables: Q LNFKREP is the aggregated quality of non-financial KPIs disclosed in the whole report in addition to executives’ compensations in Mo1; non-executives’ compensations in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; Ownership structure variables in

Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.

Table 69 Firm value (TQ) & total KPI reporting quantity


QNTKREP -0.024**

0.011

-0.024**

0.011

-0.018

0.012

-0.022*

0.012

-0.021*

0.012

-0.019*

0.01

EXCOMP 0.065 0.06

0.054 0.059


0.067 0.058

BORSIZE -0.011

0.008

-0.015*

0.008

BORCOMP -0.036 0.127

-0.21 0.133

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BORMEET -0.007

0.008

-0.009

0.007

ROLEDUAL 0.145**

0.047

0.166**

0.059

ACSIZE 0.013 0.017

0.025 0.019

ACMEET 0.009

0.018

0.014

0.013

MANGOWN -0.138 0.181

-0.206 0.147


0.112 0.114

SIZE 0.046*

0.028

0.067**

0.021

0.104***

0.026

0.061**

0.024

0.083**

0.026

0.071**

0.033

PROFITAB 0.876**

0.294

0.897**

0.306

0.841**

0.301

0.918**

0.281

0.886**

0.298

0.895**

0.279


0.062 0.084

0.072 0.059

0.065 0.069

0.067 0.078

0.05 0.064

CROSSLIST -0.006

0.041

-0.012

0.042

-0.009

0.043

-0.002

0.046

-0.009

0.041

0.004

0.043

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003 0.003

0.002 0.003

0.002 0.003

0.003 0.003

0.003 0.003

0.002 0.004

PRPLEQ_SALES 0.0001

0

0.0001

0

0.0001

0

0.0001

0

0.0001

0

0.0001

0

Constant -0.674** 0.251

-0.747** 0.333

-0.650** 0.213

-0.528** 0.197

-0.665** 0.267

-1.040*** 0.248

F 12.0*** 11.9*** 11.5*** 11.4*** 11.5*** 9.9***

Adj R-squared 0.279 0.277 0.303 0.278 0.281 0.322

Mean VIF 2.05 1.9 1.91 1.86 1.88 2.16

Max VIF 3.9 3.92 3.90 3.92 4.1 5.53

N 485 485 485 485 485 485


Dependent variable: TQ +3: TobinsQ three months after the year end. Explanatory variables: Q NTKREP is the quantity of financial and non-financial KPIs disclosed in the whole report in addition to executives’ compensations in Mo1; non-executives’ compensations in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; O wnership structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All

regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.

Table 70 Firm value (TQ) & total KPI reporting quality


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QLTKREP -0.066

0.056

-0.057

0.063

-0.006

0.074

-0.066

0.071

-0.052

0.066

-0.036

0.065

EXCOMP 0.062 0.059

0.051 0.055


0.055 0.059

BORSIZE -0.012

0.008

-0.016**

0.008

BORCOMP -0.056 0.134

-0.224 0.137

BORMEET -0.006 0.008

-0.008 0.007

ROLEDUAL 0.154**

0.048

0.169**

0.058

ACSIZE 0.015 0.017

0.028 0.02

ACMEET 0.009 0.019

0.014 0.013

MANGOWN -0.141

0.181

-0.209

0.145


0.122 0.116

SIZE 0.041 0.026

0.062**

0.022

0.099***

0.026

0.054**

0.025

0.077**

0.027

0.071**

0.032

PROFITAB 0.907**

0.286

0.927**

0.299

0.874**

0.295

0.950***

0.273

0.915**

0.292

0.921***

0.277


0.069 0.084

0.079 0.058

0.068 0.066

0.072 0.076

0.056 0.062


-0.014 0.042

-0.01 0.043

-0.005 0.045

-0.011 0.041

0.003 0.043

CASH_ASSETS

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

0.006**

0.002

CAPEX_ASSETS

0.003 0.003

0.003 0.003

0.003 0.003

0.003 0.003

0.003 0.003

0.003 0.003


0.0001 0

0.0001 0

0.0001 0

0.0001 0

0.0001 0

Constant -0.628**

0.262

-0.652*

0.359

-0.639**

0.218

-0.487**

0.202

-0.639**

0.268

-0.997***

0.26

F 11.6*** 11.5*** 11.2*** 11.0*** 11.1*** 9.6***

Adj R-squared 0.271 0.268 0.297 0.271 0.274 0.316

Mean VIF 2.06 1.89 1.92 1.85 1.88 2.17

Max VIF 3.89 3.9 3.89 3.91 4.09 5.6

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N 485 485 485 485 485 485


Dependent variable: TQ +3: TobinsQ three months after the year end. Explanatory variables: Q LTKREP is the aggregated quality of financial and non-financial KPIs disclosed in the whole report in addition to executives’ compensations in Mo1; non-executives’ compensations in Mo2; board characteristics in Mo3; audit committee characteristics in Mo4; O wnership

structure variables in Mo5; and all explanatory variables used in the analyses in Mo6. All variables are defined in Table 31. All regressions include industries dummies. Standard errors in the second line for each variable are corrected for firm and time clustering.